WO2010004781A1 - Abnormal shadow detecting device, abnormal shadow detecting method, and program - Google Patents

Abnormal shadow detecting device, abnormal shadow detecting method, and program Download PDF

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Publication number
WO2010004781A1
WO2010004781A1 PCT/JP2009/054488 JP2009054488W WO2010004781A1 WO 2010004781 A1 WO2010004781 A1 WO 2010004781A1 JP 2009054488 W JP2009054488 W JP 2009054488W WO 2010004781 A1 WO2010004781 A1 WO 2010004781A1
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region
abnormal shadow
breast
area
density
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PCT/JP2009/054488
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French (fr)
Japanese (ja)
Inventor
剛 小林
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コニカミノルタエムジー株式会社
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Priority to JP2010519667A priority Critical patent/JPWO2010004781A1/en
Publication of WO2010004781A1 publication Critical patent/WO2010004781A1/en

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/502—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of breast, i.e. mammography
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30068—Mammography; Breast

Definitions

  • the present invention relates to an abnormal shadow detection device, an abnormal shadow detection method, and a program.
  • CAD Computed-Aided Diagnosis
  • the shadow of a lesion often has a characteristic density distribution, and the CAD detects an image area estimated as a lesion based on such density characteristics as an abnormal shadow candidate area.
  • tumors and microcalcification clusters can be mentioned as characteristic features of the cancerous part of breast cancer, but on a medical image (mammography) taken of the breast, the shadow of the tumor is whitish and round with a density change close to a Gaussian distribution. Appears as a shadow.
  • a microcalcification cluster is a collection of microcalcifications (clustered), and appears on the mammography as a whitish round shadow having a substantially conical density change.
  • the great pectoral muscle taken with the breast is mainly composed of muscle (muscle tissue).
  • the breast is composed of a mammary gland and fat.
  • the mammary gland changes to fat due to the aging of the subject.
  • the mammary gland is photographed white and the fat is photographed black. That is, when the subject is young, the whole breast region is photographed white (dense breast), and when the subject is a middle age, the whole breast region is photographed black (active rest). It will be.
  • an abnormal shadow using an iris filter As described in Patent Document 1 and Patent Document 2, generally, it reacts strongly to a region that is round and has a lower concentration than the surroundings, such as a tumor shadow. That is, when a mammary gland with a high degree of circularity and a thickness appears on the image, the mammary gland of a normal tissue may be erroneously detected as an abnormal shadow.
  • a method of detecting an abnormal shadow candidate based on a curvature obtained from a curved surface indicating a density distribution of a medical image such as mammography has been studied. Abnormal shadows that appear in mammography have more X-ray absorption than normal tissues such as mammary glands and fat (that is, they are photographed white). Candidates for abnormal shadows are detected using the density difference of surrounding mammary glands as an index.
  • the abnormal shadow candidate detection method using an iris filter or the like cannot individually cope with the tissue change of the subject and cannot prevent erroneous detection.
  • the present invention has been made in view of the above problems, and an object of the present invention is to suppress the influence of tissue changes caused by aging of a subject and reduce the occurrence of false detection of abnormal shadows. .
  • an abnormal shadow detection apparatus comprises: Breast area extraction means for extracting a breast area in a breast image; A region determination unit that extracts a pectoral muscle region in the breast region extracted by the breast region extraction unit and determines a region of the extracted pectoral muscle region that refers to a concentration; An abnormal shadow candidate detecting means for detecting a candidate area of an abnormal shadow based on the density of the breast area extracted by the breast area extracting means; A density difference between the average density of the candidate area detected by the abnormal shadow candidate area detection means and the average density of the area determined by the area determination means is calculated, and the candidate area is calculated based on the calculated density difference. Determining means for determining whether or not an abnormal shadow, Is provided.
  • the abnormal shadow detection device is: A storage means for storing a reference value for determining whether or not the candidate area detected by the abnormal shadow candidate area detection means is an abnormal shadow;
  • the determination unit preferably determines that the candidate area is an abnormal shadow when the absolute value of the density difference is equal to or greater than a reference value stored in the storage unit.
  • the abnormal shadow candidate detection unit detects an abnormal shadow candidate region based on a curvature obtained from a density distribution of neighboring pixels within a predetermined region from an arbitrary pixel of interest in the breast region extracted by the breast region extraction unit. It is preferable.
  • the abnormal shadow candidate detection means detects an abnormal shadow candidate area based on a contrast obtained from a density difference between neighboring pixels within a predetermined area from an arbitrary target pixel in the breast area extracted by the breast area extraction means. It is preferable.
  • the abnormal shadow candidate detection means includes a density of a neighboring pixel, a density distribution shape, a density distribution size, and a density distribution area within a predetermined area from any target pixel in the breast area extracted by the breast area extraction means. It is preferable to detect an abnormal shadow candidate region based on any one or a plurality of features of the edges.
  • the region determination unit extracts a pectoral muscle region in the breast region extracted by the breast region extraction unit, and among the extracted pectoral muscle region, a region and / or a lesion that has been imaged by overlapping the breast and pectoral muscles It is preferable to determine a region for which density is referred to by excluding a part region.
  • the abnormal shadow detection method comprises: A breast region extraction step of extracting a breast region in a breast image; A region determination step of extracting a pectoral muscle region in the breast region extracted by the breast region extraction step, and determining a region of the extracted pectoral muscle region that refers to a concentration; An abnormal shadow candidate detection step of detecting a candidate region of an abnormal shadow based on the density of the breast region extracted by the breast region extraction step; A density difference between the average density of the candidate area detected by the abnormal shadow candidate area detection step and the average density of the area determined by the area determination step is calculated, and the candidate area is calculated based on the calculated density difference A determination step of determining whether or not the image is an abnormal shadow, Have
  • the determination step determines that the candidate area is an abnormal shadow when the absolute value of the density difference is equal to or greater than a reference value stored in a storage unit.
  • the abnormal shadow candidate detection step detects an abnormal shadow candidate region based on a curvature obtained from a density distribution of neighboring pixels within a predetermined region from an arbitrary pixel of interest in the breast region extracted by the breast region extraction step. It is preferable.
  • the abnormal shadow candidate detecting step detects an abnormal shadow candidate region based on a contrast obtained from a density difference between neighboring pixels within a predetermined region from an arbitrary pixel of interest in the breast region extracted by the breast region extracting step. It is preferable.
  • the density of neighboring pixels, the shape of the density distribution, the size of the density distribution, the region of the density distribution in the predetermined region range from any target pixel in the breast region extracted by the breast region extraction step It is preferable to detect an abnormal shadow candidate region based on any one or a plurality of features of the edges.
  • the region determination step extracts a pectoral muscle region in the breast region extracted by the breast region extraction step, and a region and / or a lesion in which the breast and pectoral muscles are imaged in the extracted pectoral muscle region. It is preferable to determine a region for which density is referred to by excluding a part region.
  • the program is Computer A breast region extraction means for extracting a breast region in a breast image; A region determination unit that extracts a pectoral muscle region in the breast region extracted by the breast region extraction unit, and determines a region whose density is referred to from the extracted pectoral muscle region; Abnormal shadow candidate detection means for detecting a candidate area for abnormal shadow based on the density of the breast area extracted by the breast area extraction means; A density difference between the average density of the candidate area detected by the abnormal shadow candidate area detection means and the average density of the area determined by the area determination means is calculated, and the candidate area is calculated based on the calculated density difference. Means for judging whether or not an abnormal shadow, To function as.
  • the program is The computer,
  • the determination means further functions as a storage means for storing a reference value for determining whether or not the candidate area detected by the abnormal shadow candidate area detection means is an abnormal shadow,
  • the determination unit preferably determines that the candidate area is an abnormal shadow when the absolute value of the density difference is equal to or greater than a reference value stored in the storage unit.
  • the abnormal shadow candidate detection unit detects an abnormal shadow candidate region based on a curvature obtained from a density distribution of neighboring pixels within a predetermined region from an arbitrary pixel of interest in the breast region extracted by the breast region extraction unit. It is preferable.
  • the abnormal shadow candidate detection means detects an abnormal shadow candidate area based on a contrast obtained from a density difference between neighboring pixels within a predetermined area from an arbitrary target pixel in the breast area extracted by the breast area extraction means. It is preferable.
  • the abnormal shadow candidate detection means includes a density of a neighboring pixel, a density distribution shape, a density distribution size, and a density distribution area within a predetermined area from any target pixel in the breast area extracted by the breast area extraction means. It is preferable to detect an abnormal shadow candidate region based on any one or a plurality of features of the edges.
  • the region determination unit extracts a pectoral muscle region in the breast region extracted by the breast region extraction unit, and among the extracted pectoral muscle region, a region and / or a lesion that has been imaged by overlapping the breast and pectoral muscles It is preferable to determine a region for which density is referred to by excluding a part region.
  • the present invention it is possible to suppress the influence of the tissue change caused by the aging of the subject and reduce the occurrence of erroneous detection of abnormal shadows.
  • FIG. 1 It is a figure which shows the abnormal shadow detection apparatus in this embodiment. It is a flowchart which shows the abnormal shadow detection process which the abnormal shadow detection apparatus shown in FIG. 1 performs. It is a schematic diagram which shows the relationship between a breast area
  • FIG. 1 shows a functional configuration example of the abnormal shadow detection apparatus 10 according to the present embodiment.
  • the abnormal shadow detection apparatus 10 includes a CPU (Central Processing Unit) 11, an I / F (InterFace) 12, an operation unit 13, a display unit 14, a communication unit 15, a RAM (Random Access Memory) 16, A ROM (Read Only Memory) 17, a printer 18, and the like are provided, and each unit is connected by a bus 19.
  • the CPU 11 reads out system programs and various processing programs stored in the ROM 17 and expands them in the RAM 16, and executes various processes including an abnormal shadow detection process described later in cooperation with the expanded programs.
  • the operation of each part of the shadow detection apparatus 10 is centrally controlled.
  • the I / F 12 is an interface for connecting to the image generation apparatus G, and inputs image data generated in the image generation apparatus G to the abnormal shadow detection apparatus 10.
  • the image generation apparatus G is an apparatus that captures a patient's breast as a subject and digitally converts the captured image to generate breast image data.
  • the image generation device G for example, a CR (Computed Radiography) device, an FPD (Flat Panel Detector) device, or the like is applicable.
  • the image generation device G generates breast image data D for one patient and inputs it to the abnormal shadow detection device 10.
  • the operation unit 13 includes a keyboard including cursor keys, numeric keys, and various function keys, and outputs an operation signal corresponding to the pressed key to the CPU 11. Note that a pointing device such as a mouse or a touch panel may be included as necessary.
  • the display unit 14 includes a monitor such as an LCD (Liquid Crystal Display) or a CRT (Cathode Ray Tube), and displays a breast image or the like in accordance with an instruction of a display signal input from the CPU 11.
  • a monitor such as an LCD (Liquid Crystal Display) or a CRT (Cathode Ray Tube)
  • LCD Liquid Crystal Display
  • CRT Cathode Ray Tube
  • the communication unit 15 includes a communication interface such as a network interface card, a modem, and a terminal adapter, and transmits / receives various information to / from external devices on the communication network.
  • the image data may be received from the image generation device G via the communication unit 15 or may be connected to an image server in a hospital via the communication unit 15.
  • the RAM 16 forms a work area for temporarily storing various programs executed by the CPU 11 and data processed by these programs.
  • the ROM 17 functions as a storage unit, and stores various programs executed by the CPU 11 and data such as parameters necessary for execution of processing by the programs or processing results. These various programs are stored in the form of readable program codes, and the CPU 11 sequentially executes operations according to the program codes.
  • Other computer-readable media other than the ROM 17 include a non-volatile memory such as a flash memory such as an SD (Secure Digital) card or a USB (Universal Serial Bus) memory, and a portable recording medium such as a CD-ROM. It is possible to apply. It is also possible to provide various data such as program data according to the present invention via a communication line by superimposing them on a carrier wave.
  • the printer 18 forms and outputs an image on a recording medium such as a film based on the image data under the control of the CPU 11.
  • the abnormal shadow detection apparatus 10 when the breast image data D is input from the image generation apparatus G, an abnormal shadow detection process described below is executed. Note that in the normal case, the image generation apparatus G captures the left and right breasts of the subject. That is, left and right breast image data D are input from the image generation device G, respectively. In the abnormal shadow detection process described below, the abnormal shadow detection process similar to that for one breast is performed for the other breast. Therefore, for convenience of explanation, only the abnormal shadow detection process for one breast image data D will be described. .
  • FIG. 2 shows a flowchart of the abnormal shadow detection process executed in the abnormal shadow detection apparatus 10. This process is realized by a software process in cooperation with the CPU 11 and a program stored in the ROM 17. Note that the pixel value of the breast image data D in the present embodiment indicates a density value. The processing described below is executed by the CPU 11.
  • FIG. 3 schematically shows the breast image data D.
  • step S1 from the breast image data D, an area where X-rays have passed through the subject (hereinafter referred to as breast area Sa) and an area where X-rays did not pass through the subject (hereinafter referred to as outside breast area Sb). Processing to sort is performed.
  • the CPU 11 functions as a breast region extracting unit.
  • a known method may be used for the breast region extraction processing performed in step S1. For example, it is extracted as in the following (1-1) to (1-3).
  • the breast image data D is 12-bit grayscale data
  • each pixel is represented by a pixel value of 0 to 4095.
  • the breast region of the breast image data D is photographed white.
  • the pixel values of the breast region Sa are concentrated between low regions (for example, 0 to 1000).
  • the area other than the subject that is, the region where the X-rays do not pass through the subject are photographed in black.
  • the pixel values of the extramammary region Sb are concentrated between high regions (eg, 4000 to 4095).
  • the breast region Sa and the extramammary region Sb are discriminated based on the tendency to binarize the low region and the high region.
  • each of the fixed sections is simply referred to as a segment).
  • the average pixel value of each segment is compared with a threshold value.
  • This threshold value is a threshold value for considering the breast region Sa, and is stored in the ROM 17 in advance.
  • Each segment is binarized by being compared with a threshold value. That is, processing is performed so that a segment indicating a pixel value smaller than the threshold is “1” and a segment indicating a pixel value larger than the threshold is “0”. Then, the binarized value of each segment is corrected based on the morphology.
  • the breast image data D can be binarized for each segment. Note that binarization may be performed for all pixels without dividing the breast image data D into segments, but in this embodiment, binarization is performed for each segment as described above in order to reduce the processing load. I will do it.
  • the breast image data D is equally divided into two in the vertical direction. That is, the breast image data D is divided into left and right parts. Among the two divided areas, a bright area (that is, an area with many white pixels) is determined. As a determination method, for example, an area having a larger number of segments having a binarized value “1” of the two left and right areas is determined as a bright area. That is, it is determined that the breast is present in the region with the larger number of white pixels.
  • a candidate for the breast region Sa is extracted from the bright regions of the breast image data D. Specifically, first, the size of the area where the binarized value of each segment is “1” is determined. If it is determined that this region is sufficiently wider than a predetermined threshold, the region is set as a candidate for the breast region Da. When it is determined that it is not wider than a predetermined threshold value, the following adjustment process is performed. Four segments (up / down / left / right segments) surrounding a certain segment (referred to as a target segment) are defined as segments of interest.
  • the segment of interest has a bipolar value. Of these, “1” is set. On the contrary, if the average pixel value of the target segment is larger than a predetermined threshold value, “0” of the bipolar values is set in the segment of interest.
  • the adjustment process is performed in this manner, and it is determined again whether the area of the binarized value “1” is sufficiently wide. If a candidate region for the breast region Sa is still not found, the other region (the other of the regions obtained by dividing the breast image data D into two regions, that is, the region that is not bright) is searched. If no candidate is still found, the whole breast image data D is searched. The candidate extracted in this way is defined as a breast region Sa.
  • the pectoral muscle region M1 is extracted from the breast region Sa calculated in step S1 (step S2).
  • a known method may be used for the extraction processing of the pectoral muscle region M1 performed in step S2.
  • the pectoral muscle region M1 extraction process performed in step S2 is extracted as in the following (2-1) to (2-5).
  • the position of each pixel in the breast image data D1 is represented by coordinates (X, Y) with the left-right direction of the breast in the breast image data D1 as the X axis and the vertical direction as the Y axis.
  • the pixel value of the coordinates (X, Y) in the breast image data D1 is represented as V (X, Y).
  • the coordinates of the image end in the X-axis direction are represented as X max
  • the image end in the Y-axis direction is represented as Y max .
  • a skin line SL that is a boundary point between the breast region Sa and the extramammary region Sb in the breast image data D1 is extracted.
  • the boundary between the breast region Sa and the extramammary region Sb calculated in step S1 may be the skin line SL.
  • the skin line SL is as follows. To extract. For each Y coordinate (0 to Y max ) of the breast image data D1, a search is performed in the X-axis direction, and a coordinate S (Y) that maximizes V (X, Y) is extracted. Thereby, the edge in each Y coordinate of the breast image data D1 is extracted. As shown in FIG. 4A, the extracted edge is a boundary point between the breast region Sa and the extramammary region Sb in the breast image data D1, and constitutes a skin line SL.
  • Search lines la0 to la30 having a length of 5 are set.
  • the average value of the pixel values on the search lines la0 to la30 is calculated.
  • the reference point of the search line having the maximum calculated average value is determined as the pectoral muscle line search start point B.
  • the pectoral muscle line search start point B is close to the right end (X max ) of the image, for example, from the right end to 10 pixels, it is determined that there is no pectoral muscle region M1.
  • the calculated maximum average value is smaller than a predetermined threshold value, for example, 300, it is determined that there is no pectoral muscle region M1.
  • a predetermined threshold value for example, 300
  • it is determined that there is no pectoral muscle region M1 for example, an error message or the like is displayed on the display unit 14, and the abnormal shadow detection process ends.
  • FIG. 4B shows an enlarged view near the pectoral muscle line search start point B.
  • the width of the breast image data D in the Y-axis direction is 1/5 in increments of 1 °.
  • Search lines lb0 to lb18 having a length are set. Next, the average value of the pixel values on the search lines lb0 to lb18 is calculated.
  • the search line lbn (n is an integer from 0 to 18) having the maximum calculated average value is determined as the pectoral muscle line L.
  • similar processing is performed using a point that is 1/10 of the width in the Y-axis direction from the pectoral muscle line search start point B as a base point.
  • the same processing is performed with a point that is 1/10 of the width in the Y-axis direction from the point set as the previous base point.
  • the pectoral muscle line L is extracted.
  • a predetermined threshold for example, 300
  • a region where density is not measured (hereinafter referred to as a density reference exclusion region) is determined and excluded from the pectoral muscle region M1 extracted in step S2 (step S3).
  • the pectoral muscle region M1 is extracted from the breast region Sa in step S2, and a region in the pectoral muscle region M1 near the pectoral muscle line L (hereinafter referred to as a mammary gland overlap region T1) is a pectoral muscle region. Not taken into account as the concentration of M1. This is because the mammary gland overlap region T1 may be a region in which the mammary gland and the pectoral muscle are imaged due to poor positioning during imaging.
  • lymphoproliferative region T2 a region in the vicinity of this lesion (hereinafter referred to as lymphoproliferative region T2) is excluded from the density calculation. This is because the concentration of the lymph hypertrophy region T2 and the concentration of tissues such as normal lymph are different.
  • the aforementioned mammary gland overlap region T1 and lymphoproliferative region T2 are determined as the concentration reference exclusion region.
  • the concentration reference exclusion region may be a region that is not regarded as a normal muscle tissue, and is not limited to that in the present embodiment.
  • the ROM 17 may hold a lesion area other than lympho-hypertrophy and a density reference exclusion area preset by the user.
  • step S3 The confirmation and exclusion processing of the mammary gland overlap region T1 and the lymphoproliferative region T2 performed in step S3 is calculated as follows (3-1) to (3-3).
  • the coordinates of the pixel on the pectoral muscle line L are (X max ⁇ X muscle [k], k).
  • k is an arbitrary integer from 0 to Y max .
  • the coordinate of the pectoral muscle line L with the value of Y being 0 (hereinafter referred to as coordinate P1) is (X max -X muscle [0], 0).
  • the value of k when X muscle [k] is 0 is assumed to be Y muscleMAX . That is, in the pectoral muscle line L, the coordinates of the X value of X max (hereinafter referred to as coordinates P2) are (X max , Y muscleMAX ).
  • the coordinate P2 and the X coordinate are the same, and the value of the Y coordinate is 2/3 of the Y coordinate of P2 (hereinafter referred to as the coordinate P3).
  • An area delimited by a straight line connecting P1 (shown by a broken line in FIG. 5 and hereinafter referred to as a straight line L1) is used. That is, in the pectoral muscle region M1, a region having the same X coordinate value and a larger Y coordinate value than the straight line L1 is defined as a mammary gland overlap region T1.
  • the method for determining the mammary gland overlap region T1 is not limited to this as long as it is determined from the pectoral muscle region M1, the pectoral muscle line L, and the like.
  • the pectoral muscle region M2 excluding the mammary gland overlap region T1 from the pectoral muscle region M1 is extracted as a region surrounded by the following expression.
  • lymphatic hypertrophy region T2 included in the pectoral muscle region M2
  • a known method may be used for determining and excluding the lymph hypertrophy region T2.
  • a method of determining the lymph hypertrophy region T2 by performing a determination using a curvature as described below may be used.
  • the curvature is composed of signal components in three directions (three axes of X, Y, and Z) of the coordinates (X, Y) of the pixel included in the pectoral muscle region M2 and the pixel value of the pixel, that is, the density (Z). It is calculated by approximating the normal section of the pixel of interest with a circle from the curved surface obtained from the density distribution and obtaining the radius of the circle.
  • the curvature is an index indicating whether the curved surface is convex or concave. That is, the larger the curvature in the positive direction, the more concave the curved surface, and the larger the curvature value in the negative direction, the convex shape. Therefore, the larger the absolute value of the curvature, the greater the density gradient in the vicinity of the target pixel.
  • Abnormal shadows such as lymphatic hypertrophy are generally classified into concave shapes.
  • the pectoral muscle region M2 is divided into predetermined small regions, and an average value of curvature, a maximum value of curvature, a minimum value of curvature, and the like are calculated as feature amounts for each small region. This feature amount is compared with a preset threshold value.
  • a small region having a feature amount equal to or greater than a threshold value, that is, a large concave region is calculated as a candidate region for lymphoproliferation.
  • the candidate region for lymph hypertrophy is approximated to a circle and the diameter is 10 mm or less, it is recognized as normal lymph.
  • the candidate region is determined as the lymphoproliferative region T2.
  • the lymphoproliferative region T2 calculated as described above is excluded from the pectoral muscle region M2 (hereinafter, this region is referred to as a concentration reference region M3).
  • FIG. 5B shows a lymphoproliferative region T2 and a concentration reference region M3.
  • a region obtained by excluding the lymph hypertrophy region T2 from the pectoral muscle region M2 is the concentration reference region M3.
  • the CPU 11 functions as an area determination unit by the processing in step S2 and step S3.
  • the density reference area M3 may be determined based on the pectoral muscle area T1, and the process of excluding the density reference exclusion area from the density reference area M3 in step S3 is not essential.
  • step S4 the average density D MuscleAve of the density reference region M3 calculated in step S3 is calculated (step S4). Specifically, the pixels included in the density reference area M3 are counted, and the number N of pixels in the density reference area M3 is acquired. A pixel value (density) of an arbitrary pixel included in the density reference region M3 is defined as D (x, y). The average density D MuscleAve is calculated by dividing the sum of the pixel values of the pixels included in the density reference area M3 by the number N of pixels included in the density reference area M3 as in the following equation.
  • a candidate for an abnormal shadow in the breast region Sa is detected (step S5).
  • abnormal shadow candidates are obtained based on the curvature of the density of the pixels included in the breast region Sa. Calculated.
  • step S5 another method may be used as the method for detecting abnormal shadow candidates in step S5.
  • the method for detecting abnormal shadow candidates in step S5 may be used.
  • Japanese Patent Application Laid-Open No. 10-91758 that detects an abnormal shadow from the density distribution shape and the like
  • Japanese Patent Application Laid-Open No. 09-508815 that detects an abnormal shadow based on a contrast difference in density. The method may be used.
  • the iris filter process which is an effective technique for detecting a mass shadow that is one of the characteristic forms particularly in breast cancer, is performed in step S5 of the present embodiment. May be applied.
  • the mass shadow tends to have a slightly lower density value than the surrounding image portion. Therefore, the density value distribution decreases in the density value from the substantially circular periphery toward the center. It has a gradient of density value.
  • the gradient line is concentrated toward the center of the tumor mass. That is, abnormal shadow candidates can be detected based on the shape and size of the density distribution and the features of the edges of the density distribution area.
  • the iris filter calculates the gradient of the image signal typified by this density value as a gradient vector and outputs the degree of concentration of the gradient vector. As a result, a region having a density gradient close to a circle is detected as an abnormal shadow candidate. In step S5, an abnormal shadow may be detected based on the concentration degree of the gradient vector calculated by the iris filter process.
  • an abnormal shadow may be detected using a difference in density contrast.
  • multiple gray level threshold processing is performed on the breast region Sa, accurate region increase and feature analysis are performed to increase specificity, and an abnormal shadow candidate is detected based on the emission angle of the pixel of interest.
  • An abnormal shadow may be detected by determining whether the periphery of the tumor edge, the inside of the tumor, or the periphery of the tumor based on the contrast difference in density calculated by the cumulative edge gradient orientation histogram analysis.
  • the CPU 11 functions as an abnormal shadow candidate detecting means by the processing in step S5.
  • FIG. 6A schematically shows the abnormal shadow candidate areas detected in step S5.
  • the abnormal shadow candidate detected in step S5 is defined as an abnormal shadow candidate region T3. That is, since the curvature of density of the pixels included in the abnormal shadow candidate region T3 is equal to or greater than a certain threshold value, the abnormal shadow candidate is detected in step S5.
  • step S6 the average density of the abnormal shadow candidate region T3 detected in step S5 is calculated.
  • step S6 the method of calculating the average density in step S6 will be described.
  • the following two methods (6-1) and (6-2) can be used to measure the average density of the abnormal shadow candidate region T3.
  • the curvature of the region near the edge of the abnormal shadow candidate region T3 is close to the threshold value.
  • both of the two methods described below are areas where the possibility of abnormal shadows is relatively low in the vicinity of the edge, and therefore the purpose is to measure the average density by excluding the area near the edge. Yes.
  • FIG. 6B schematically shows a case where the edge of abnormal shadow candidate region T3 is approximated by an ellipse (hereinafter simply referred to as approximate ellipse C1). .
  • the approximate ellipse C1 is specified by the major axis r max , the minor axis r min , and the center point O (c, d).
  • the approximate ellipse C1 may be created using any method, for example, by approximating the coordinates of the pixels constituting the edge of the abnormal shadow candidate region T3 by the least square method.
  • 6C schematically shows a circle C2 having a radius r min centered on the center point O (c, d) of the approximate ellipse C1.
  • the area surrounded by the circle C2 may be measured as the average density of the abnormal shadow candidate area T3.
  • the average pixel value of the pixels surrounded by the following formula is measured. The average pixel value is calculated by dividing the sum of the pixel values D ij of the pixels included in the circle C2 by the number of pixels.
  • FIG. 6D schematically shows an area excluding the area near the edge of the abnormal shadow candidate area T3.
  • the coordinates of the edge are (X marginal , Y marginal )
  • the coordinates of the edge are t (shown as an arbitrary constant from 0 to 1) from the center point O (c, d).
  • the average density in the area surrounded by the doubled coordinates is calculated.
  • the average density of the region surrounded by the coordinates (t (X marginal ⁇ c), t (Y marginal ⁇ d)) may be calculated.
  • This average density is calculated from the average pixel value as in (6-1).
  • the average density of the abnormal shadow candidate area T3 calculated as described above is defined as D AbnormalAve .
  • the average density D AbnormalAve of the abnormal shadow candidate area T3 acquired in step S6 is compared with D MuscleAve , so that it is finally determined whether or not the abnormal shadow candidate area T3 is an abnormal shadow.
  • Step S7 the determination is made based on whether or not the absolute value of the difference between the average concentrations D AbnormalAve and D MuscleAve is greater than or equal to a predetermined reference value Th pickup . That is, if the following expression is satisfied, the abnormal shadow candidate area T3 is not determined to be an abnormal shadow in step S7.
  • the abnormal shadow candidate area T3 determined to be an abnormal shadow in step S7 may be superimposed on the breast image data D and displayed on the display unit 14.
  • the CPU 11 functions as a determination unit by the processing in step S7, and the reference value Th pickup is a reference value for determining that the abnormal shadow candidate area T3 is an abnormal shadow.
  • the reference value Th pickup used in step S7 is previously stored in the ROM 17 as described above.
  • the reference value Th pickup may be determined in any way. For example, by displaying the following density relationship table on the display unit 14, the reference value Th pickup may be determined by the judgment of the user (that is, an interpreting doctor or the like). It's okay.
  • FIG. 7 shows a concentration relationship table used when determining the reference value Th pickup .
  • the pectoral muscle density band, the mass density band, the high density mass density band, the mammary gland density band, and the fat density band are displayed on the display unit 14.
  • the abnormal shadow candidates detected by the abnormal shadow detection process executed in the past in the abnormal shadow detection apparatus 10 are classified according to the judgment of the user. That is, the abnormal shadow candidates detected by the abnormal shadow detection process may include erroneously detected normal tissues.
  • the user visually recognizes the candidate for an abnormal shadow displayed on the display unit 14, and diagnoses whether the candidate is a lesion such as a tumor or a normal mammary gland.
  • regions of abnormal shadow candidates are divided into tissues (for example, pectoral muscles, tumors, high-density tumors, mammary glands, and fats), and the average density and tissues of the regions are associated with each other and stored in the ROM 17.
  • tissues for example, pectoral muscles, tumors, high-density tumors, mammary glands, and fats
  • the average density and tissues of the regions are associated with each other and stored in the ROM 17.
  • the average density for each tissue stored in the ROM 17 is referred to, and the average density of the abnormal shadow candidates stored in the ROM 17 and the standard are stored for each tissue classified by the user. Deviation is displayed.
  • an area that is detected as an abnormal shadow by the abnormal shadow detection process, but the user visually recognizes the area displayed on the display unit 16 and determines that the area is a mammary gland is represented as a “breast density band”.
  • the “mammary gland concentration band” indicates that the concentration is concentrated in the vicinity of “1675 ⁇ 185”.
  • 1675 indicates an average value of the density of an area detected as an abnormal shadow but determined as a mammary gland by the user
  • “185” indicates a standard deviation. The same applies to other organizations.
  • the user determines the reference value Th pickup based on the density relationship table.
  • the determined reference value Th pickup is stored in the ROM 17 and used in step S7 of the abnormal shadow detection process to be executed next.
  • the pectoral muscle concentration is distributed in the vicinity of “1141”
  • the high-concentration mass concentration band is distributed in the vicinity of “1305”
  • the reference value Th pickup is “1305-1114”. This can be determined by setting “164” that is “.
  • the reference value Th pickup may be calculated based on the tissue in which the abnormal shadow candidate detected by the abnormal shadow detection process is finally determined by the user and the density of the region.
  • the CPU 11 may calculate the reference value Th pickup every time the abnormal shadow detection process is executed based on the equation.
  • the abnormal shadow detection apparatus 10 in the present embodiment by comparing the density of the pectoral muscle region with the concentration of the region detected as the abnormal shadow candidate, whether the region is an abnormal shadow or not. It can be determined whether or not. In other words, based on the density of the pectoral muscle region with little tissue change due to age, it is possible to determine whether the subject's abnormal shadow candidate is an abnormal shadow or an erroneously detected one. Abnormal shadow detection with little influence of tissue change can be performed.
  • the density reference exclusion region is determined from the pectoral muscle region, and the concentration of the concentration reference exclusion region is not calculated as the average concentration of the pectoral muscle region. That is, since the average density of the pectoral muscle area excluding the area that does not show normal density, such as an imaging error due to poor positioning or a lesion, is calculated, the accuracy of abnormal shadow detection can be further improved.
  • step S7 in the present embodiment the difference between the average density of the pectoral muscle region and the average density of the abnormal shadow candidate is compared with a threshold value to determine whether or not the abnormal shadow candidate is an abnormal shadow. What is necessary is just to judge by the average density of an area
  • each device constituting the abnormal shadow detection device 10 can be changed as appropriate without departing from the spirit of the present invention.

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Abstract

The influence of a tissue variation due to the aging of a subject being examined is lessened, and false detection of an abnormal shadow is reduced. A CPU (11) of an abnormal shadow detecting apparatus (10) extracts a breast region (Sa) in breast image data (D) and extracts a pectoralis region (M1) in the extracted breast region (Sa). The CPU (11) detects a candidate of an abnormal shadow according to the density of the breast region (Sa) and judges whether or not the candidate region is an abnormal shadow on the basis of the difference between the density of the candidate region and the density of a density reference region (M3) used as a reference region in the pectoralis region (M1).

Description

異常陰影検出装置、異常陰影検出方法、及びプログラムAbnormal shadow detection apparatus, abnormal shadow detection method, and program
　本発明は、異常陰影検出装置、異常陰影検出方法、及びプログラムに関する。 The present invention relates to an abnormal shadow detection device, an abnormal shadow detection method, and a program.
　医療の分野においては、医用画像のデジタル化が実現され、ＣＲ（Computed Radiography）装置等により生成された医用画像データをモニタに表示する。このモニタに表示された医用画像を医師が読影して、病変部の状態や経時変化を観察して診断を行っている。 In the medical field, digitalization of medical images is realized, and medical image data generated by a CR (Computed Radiography) device or the like is displayed on a monitor. A doctor interprets a medical image displayed on the monitor, and diagnoses by observing the state of a lesioned part or a change with time.
　従来、このような医師の読影に対する負荷軽減を目的として、上記医用画像データを画像処理することにより、画像上に現れた病変部の陰影を異常陰影候補として自動的に検出するコンピュータ診断支援装置（Computed-Aided Diagnosis；以下、ＣＡＤという）と呼ばれる医用画像処理装置が開発されている。 Conventionally, for the purpose of reducing the burden on the interpretation of such doctors, a computer diagnosis support apparatus that automatically detects the shadow of a lesion appearing on an image as an abnormal shadow candidate by performing image processing on the medical image data ( A medical image processing apparatus called Computed-Aided Diagnosis (hereinafter referred to as CAD) has been developed.
　病変部の陰影は、特徴的な濃度分布を有することが多く、ＣＡＤは、このような濃度特性に基づいて病変部と推測される画像領域を異常陰影候補領域として検出するものである。例えば、乳癌の癌化部分の特徴的なものとして腫瘤、微小石灰化クラスタが挙げられるが、***を撮影した医用画像（マンモグラフィ）上では、腫瘤陰影はガウス分布に近い濃度変化をもった白っぽく丸い陰影として現れる。一方、微小石灰化クラスタは、微小石灰化した部分が集まって（クラスタ化して）存在するものであり、マンモグラフィ上では略円錐構造の濃度変化を持った白っぽく丸い陰影として現れる。 The shadow of a lesion often has a characteristic density distribution, and the CAD detects an image area estimated as a lesion based on such density characteristics as an abnormal shadow candidate area. For example, tumors and microcalcification clusters can be mentioned as characteristic features of the cancerous part of breast cancer, but on a medical image (mammography) taken of the breast, the shadow of the tumor is whitish and round with a density change close to a Gaussian distribution. Appears as a shadow. On the other hand, a microcalcification cluster is a collection of microcalcifications (clustered), and appears on the mammography as a whitish round shadow having a substantially conical density change.
　上記ＣＡＤでは、検出目的とする病変種類に応じて様々な検出アルゴリズムが開発されており、腫瘤陰影の検出に最適なアルゴリズムとしてはアイリスフィルタを用いた手法等が提案されている（例えば、特許文献１、特許文献２参照）。また、微小石灰化クラスタ陰影の検出に最適なアルゴリズムとしてはモルフォルジーフィルタを用いた手法等が提案されている。
特開平８－２６３６４１号公報 特開平１０－９１７５８号公報 In the CAD, various detection algorithms have been developed depending on the type of lesion to be detected, and a method using an iris filter has been proposed as an optimal algorithm for detecting a tumor shadow (for example, Patent Documents). 1, see Patent Document 2). In addition, a method using a morphological filter has been proposed as an optimal algorithm for detecting a microcalcification cluster shadow.
Japanese Patent Laid-Open No. 8-263642 JP-A-10-91758
　一方、デジタルマンモグラフィのＭＬＯ撮影（内外斜位方向撮影）においては、被写体の***を圧迫して撮影する。撮影領域には、***だけでなく大胸筋も入り込むような圧迫を行い、***を撮影するようにポジショニングを行うことが奨励されている。 On the other hand, in digital mammography MLO photography (inside / outside oblique direction photography), the subject's breast is pressed and photographed. It is encouraged to position the radiographing area so that not only the breast but also the greater pectoral muscles enter the radiographing area.
　***と共に撮影される大胸筋は、主に筋肉（筋組織）から構成される。***は乳腺及び脂肪等から構成される。乳腺は、被写体の加齢に起因して脂肪へと変化する。
　マンモグラフィにおける撮影では、乳腺は白く撮影され、脂肪は黒く撮影される。つまり、被写体が若年期である場合には全体的に***の領域は白く撮影（デンスブレスト）され、被写体が壮年期である場合には全体的に***の領域は黒く撮影（ファティブレスト）されることになる。 The great pectoral muscle taken with the breast is mainly composed of muscle (muscle tissue). The breast is composed of a mammary gland and fat. The mammary gland changes to fat due to the aging of the subject.
In mammography, the mammary gland is photographed white and the fat is photographed black. That is, when the subject is young, the whole breast region is photographed white (dense breast), and when the subject is a middle age, the whole breast region is photographed black (active rest). It will be.
　特許文献１、特許文献２に記載のような、アイリスフィルタを用いて異常陰影を検出する場合、一般的には腫瘤陰影のように丸く周囲に比べて低濃度である領域に強く反応する。つまり、円形度が高く厚みのある乳腺が塊状となって画像上に現れている場合、正常組織の乳腺をも異常陰影として誤検出してしまう場合がある。このような問題を解決するために、マンモグラフィ等の医用画像の濃度分布を示す曲面から取得される曲率に基づいて、異常陰影候補を検出する方法が検討されている。マンモグラフィに写る異常陰影は、乳腺や脂肪といった正常組織よりもＸ線の吸収量が多い（つまり、白く撮影される）。周囲の乳腺の濃度差を指標として異常陰影の候補を検出する。 When detecting an abnormal shadow using an iris filter as described in Patent Document 1 and Patent Document 2, generally, it reacts strongly to a region that is round and has a lower concentration than the surroundings, such as a tumor shadow. That is, when a mammary gland with a high degree of circularity and a thickness appears on the image, the mammary gland of a normal tissue may be erroneously detected as an abnormal shadow. In order to solve such a problem, a method of detecting an abnormal shadow candidate based on a curvature obtained from a curved surface indicating a density distribution of a medical image such as mammography has been studied. Abnormal shadows that appear in mammography have more X-ray absorption than normal tissues such as mammary glands and fat (that is, they are photographed white). Candidates for abnormal shadows are detected using the density difference of surrounding mammary glands as an index.
　しかしながら、マンモグラフィにおいては、先述したような被写体の加齢に起因する組織変化の影響がある。つまり、アイリスフィルタ等を使用した異常陰影の候補検出方法では、被写体の組織変化に個別に対応することができず、誤検出を防止することができない場合がある。 However, in mammography, there is an influence of tissue change caused by aging of the subject as described above. In other words, the abnormal shadow candidate detection method using an iris filter or the like cannot individually cope with the tissue change of the subject and cannot prevent erroneous detection.
　本発明は上記課題に鑑みてされたものであり、その目的とするところは、被写体の加齢に起因する組織変化の影響を抑制し、異常陰影の誤検出の発生を低減化することである。 The present invention has been made in view of the above problems, and an object of the present invention is to suppress the influence of tissue changes caused by aging of a subject and reduce the occurrence of false detection of abnormal shadows. .
　以上の課題を解決するために、本発明の第１の側面によれば、異常陰影検出装置は、
　***画像における***領域を抽出する***領域抽出手段と、
　前記***領域抽出手段によって抽出された***領域における胸筋領域を抽出し、当該抽出された胸筋領域のうち濃度を参照する領域を決定する領域決定手段と、
　前記***領域抽出手段によって抽出された***領域の濃度に基づいて異常陰影の候補領域を検出する異常陰影候補検出手段と、
　前記異常陰影候補領域検出手段によって検出された候補領域の平均濃度と、前記領域決定手段によって決定された領域の平均濃度との濃度差を算出し、当該算出された濃度差に基づいて前記候補領域を異常陰影であるか否かを判断する判断手段と、
　を備える。 In order to solve the above problems, according to the first aspect of the present invention, an abnormal shadow detection apparatus comprises:
Breast area extraction means for extracting a breast area in a breast image;
A region determination unit that extracts a pectoral muscle region in the breast region extracted by the breast region extraction unit and determines a region of the extracted pectoral muscle region that refers to a concentration;
An abnormal shadow candidate detecting means for detecting a candidate area of an abnormal shadow based on the density of the breast area extracted by the breast area extracting means;
A density difference between the average density of the candidate area detected by the abnormal shadow candidate area detection means and the average density of the area determined by the area determination means is calculated, and the candidate area is calculated based on the calculated density difference. Determining means for determining whether or not an abnormal shadow,
Is provided.
　前記異常陰影検出装置は、
　前記判断手段が前記異常陰影候補領域検出手段によって検出された候補領域を異常陰影であるか否かを判断するための基準値を記憶する記憶手段を更に備え、
　前記判断手段は、前記濃度差の絶対値が前記記憶手段に記憶された基準値以上である場合に、前記候補領域を異常陰影であると判断することが好ましい。 The abnormal shadow detection device is:
A storage means for storing a reference value for determining whether or not the candidate area detected by the abnormal shadow candidate area detection means is an abnormal shadow;
The determination unit preferably determines that the candidate area is an abnormal shadow when the absolute value of the density difference is equal to or greater than a reference value stored in the storage unit.
　前記異常陰影候補検出手段は、前記***領域抽出手段によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度分布から得られる曲率に基づいて異常陰影の候補領域を検出することが好ましい。 The abnormal shadow candidate detection unit detects an abnormal shadow candidate region based on a curvature obtained from a density distribution of neighboring pixels within a predetermined region from an arbitrary pixel of interest in the breast region extracted by the breast region extraction unit. It is preferable.
　前記異常陰影候補検出手段は、前記***領域抽出手段によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度差から得られるコントラストに基づいて異常陰影の候補領域を検出することが好ましい。 The abnormal shadow candidate detection means detects an abnormal shadow candidate area based on a contrast obtained from a density difference between neighboring pixels within a predetermined area from an arbitrary target pixel in the breast area extracted by the breast area extraction means. It is preferable.
　前記異常陰影候補検出手段は、前記***領域抽出手段によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度、濃度分布の形状、濃度分布の大きさ、濃度分布の領域の辺縁のいずれか一つまたは複数の特徴に基づいて異常陰影の候補領域を検出することが好ましい。 The abnormal shadow candidate detection means includes a density of a neighboring pixel, a density distribution shape, a density distribution size, and a density distribution area within a predetermined area from any target pixel in the breast area extracted by the breast area extraction means. It is preferable to detect an abnormal shadow candidate region based on any one or a plurality of features of the edges.
　前記領域決定手段は、前記***領域抽出手段によって抽出された***領域における胸筋領域を抽出し、当該抽出された胸筋領域のうち***と胸筋が重なって撮影がなされた領域及び／又は病変部の領域を除外して濃度を参照する領域を決定することが好ましい。 The region determination unit extracts a pectoral muscle region in the breast region extracted by the breast region extraction unit, and among the extracted pectoral muscle region, a region and / or a lesion that has been imaged by overlapping the breast and pectoral muscles It is preferable to determine a region for which density is referred to by excluding a part region.
　本発明の第２の側面によれば、異常陰影検出方法は、
　***画像における***領域を抽出する***領域抽出工程と、
　前記***領域抽出工程によって抽出された***領域における胸筋領域を抽出し、当該抽出された胸筋領域のうち濃度を参照する領域を決定する領域決定工程と、
　前記***領域抽出工程によって抽出された***領域の濃度に基づいて異常陰影の候補領域を検出する異常陰影候補検出工程と、
　前記異常陰影候補領域検出工程によって検出された候補領域の平均濃度と、前記領域決定工程によって決定された領域の平均濃度との濃度差を算出し、当該算出された濃度差に基づいて前記候補領域を異常陰影であるか否かを判断する判断工程と、
　を有する。 According to the second aspect of the present invention, the abnormal shadow detection method comprises:
A breast region extraction step of extracting a breast region in a breast image;
A region determination step of extracting a pectoral muscle region in the breast region extracted by the breast region extraction step, and determining a region of the extracted pectoral muscle region that refers to a concentration;
An abnormal shadow candidate detection step of detecting a candidate region of an abnormal shadow based on the density of the breast region extracted by the breast region extraction step;
A density difference between the average density of the candidate area detected by the abnormal shadow candidate area detection step and the average density of the area determined by the area determination step is calculated, and the candidate area is calculated based on the calculated density difference A determination step of determining whether or not the image is an abnormal shadow,
Have
　前記判断工程は、前記濃度差の絶対値が記憶手段に記憶された基準値以上である場合に、前記候補領域を異常陰影であると判断することが好ましい。 Preferably, the determination step determines that the candidate area is an abnormal shadow when the absolute value of the density difference is equal to or greater than a reference value stored in a storage unit.
　前記異常陰影候補検出工程は、前記***領域抽出工程によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度分布から得られる曲率に基づいて異常陰影の候補領域を検出することが好ましい。 The abnormal shadow candidate detection step detects an abnormal shadow candidate region based on a curvature obtained from a density distribution of neighboring pixels within a predetermined region from an arbitrary pixel of interest in the breast region extracted by the breast region extraction step. It is preferable.
　前記異常陰影候補検出工程は、前記***領域抽出工程によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度差から得られるコントラストに基づいて異常陰影の候補領域を検出することが好ましい。 The abnormal shadow candidate detecting step detects an abnormal shadow candidate region based on a contrast obtained from a density difference between neighboring pixels within a predetermined region from an arbitrary pixel of interest in the breast region extracted by the breast region extracting step. It is preferable.
　前記異常陰影候補検出工程は、前記***領域抽出工程によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度、濃度分布の形状、濃度分布の大きさ、濃度分布の領域の辺縁のいずれか一つまたは複数の特徴に基づいて異常陰影の候補領域を検出することが好ましい。 In the abnormal shadow candidate detection step, the density of neighboring pixels, the shape of the density distribution, the size of the density distribution, the region of the density distribution in the predetermined region range from any target pixel in the breast region extracted by the breast region extraction step It is preferable to detect an abnormal shadow candidate region based on any one or a plurality of features of the edges.
　前記領域決定工程は、前記***領域抽出工程によって抽出された***領域における胸筋領域を抽出し、当該抽出された胸筋領域のうち***と胸筋が重なって撮影がなされた領域及び／又は病変部の領域を除外して濃度を参照する領域を決定することが好ましい。 The region determination step extracts a pectoral muscle region in the breast region extracted by the breast region extraction step, and a region and / or a lesion in which the breast and pectoral muscles are imaged in the extracted pectoral muscle region. It is preferable to determine a region for which density is referred to by excluding a part region.
　本発明の第３の側面によれば、プログラムは、
　コンピュータを、
　***画像における***領域を抽出する***領域抽出手段、
　前記***領域抽出手段によって抽出された***領域における胸筋領域を抽出し、当該抽出された胸筋領域のうち濃度を参照する領域を決定する領域決定手段、
　前記***領域抽出手段によって抽出された***領域の濃度に基づいて異常陰影の候補領域を検出する異常陰影候補検出手段、
　前記異常陰影候補領域検出手段によって検出された候補領域の平均濃度と、前記領域決定手段によって決定された領域の平均濃度との濃度差を算出し、当該算出された濃度差に基づいて前記候補領域を異常陰影であるか否かを判断する判断手段、
　として機能させる。 According to a third aspect of the present invention, the program is
Computer
A breast region extraction means for extracting a breast region in a breast image;
A region determination unit that extracts a pectoral muscle region in the breast region extracted by the breast region extraction unit, and determines a region whose density is referred to from the extracted pectoral muscle region;
Abnormal shadow candidate detection means for detecting a candidate area for abnormal shadow based on the density of the breast area extracted by the breast area extraction means;
A density difference between the average density of the candidate area detected by the abnormal shadow candidate area detection means and the average density of the area determined by the area determination means is calculated, and the candidate area is calculated based on the calculated density difference. Means for judging whether or not an abnormal shadow,
To function as.
　前記プログラムは、
　前記コンピュータを、
　前記判断手段が前記異常陰影候補領域検出手段によって検出された候補領域を異常陰影であるか否かを判断するための基準値を記憶する記憶手段として更に機能させ、
　前記判断手段は、前記濃度差の絶対値が前記記憶手段に記憶された基準値以上である場合に、前記候補領域を異常陰影であると判断することが好ましい。 The program is
The computer,
The determination means further functions as a storage means for storing a reference value for determining whether or not the candidate area detected by the abnormal shadow candidate area detection means is an abnormal shadow,
The determination unit preferably determines that the candidate area is an abnormal shadow when the absolute value of the density difference is equal to or greater than a reference value stored in the storage unit.
　前記異常陰影候補検出手段は、前記***領域抽出手段によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度分布から得られる曲率に基づいて異常陰影の候補領域を検出することが好ましい。 The abnormal shadow candidate detection unit detects an abnormal shadow candidate region based on a curvature obtained from a density distribution of neighboring pixels within a predetermined region from an arbitrary pixel of interest in the breast region extracted by the breast region extraction unit. It is preferable.
　前記異常陰影候補検出手段は、前記***領域抽出手段によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度差から得られるコントラストに基づいて異常陰影の候補領域を検出することが好ましい。 The abnormal shadow candidate detection means detects an abnormal shadow candidate area based on a contrast obtained from a density difference between neighboring pixels within a predetermined area from an arbitrary target pixel in the breast area extracted by the breast area extraction means. It is preferable.
　前記異常陰影候補検出手段は、前記***領域抽出手段によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度、濃度分布の形状、濃度分布の大きさ、濃度分布の領域の辺縁のいずれか一つまたは複数の特徴に基づいて異常陰影の候補領域を検出することが好ましい。 The abnormal shadow candidate detection means includes a density of a neighboring pixel, a density distribution shape, a density distribution size, and a density distribution area within a predetermined area from any target pixel in the breast area extracted by the breast area extraction means. It is preferable to detect an abnormal shadow candidate region based on any one or a plurality of features of the edges.
　前記領域決定手段は、前記***領域抽出手段によって抽出された***領域における胸筋領域を抽出し、当該抽出された胸筋領域のうち***と胸筋が重なって撮影がなされた領域及び／又は病変部の領域を除外して濃度を参照する領域を決定することが好ましい。 The region determination unit extracts a pectoral muscle region in the breast region extracted by the breast region extraction unit, and among the extracted pectoral muscle region, a region and / or a lesion that has been imaged by overlapping the breast and pectoral muscles It is preferable to determine a region for which density is referred to by excluding a part region.
　本発明によれば、被写体の加齢に起因する組織変化の影響を抑制し、異常陰影の誤検出の発生を低減化させることができる。 According to the present invention, it is possible to suppress the influence of the tissue change caused by the aging of the subject and reduce the occurrence of erroneous detection of abnormal shadows.
本実施形態における異常陰影検出装置を示す図である。It is a figure which shows the abnormal shadow detection apparatus in this embodiment. 図１に示す異常陰影検出装置が実行する異常陰影検出処理を示すフローチャートである。It is a flowchart which shows the abnormal shadow detection process which the abnormal shadow detection apparatus shown in FIG. 1 performs. ***領域と***外領域の関係を示す模式図である。It is a schematic diagram which shows the relationship between a breast area | region and an area | region outside a breast. ***画像を座標で表現した模式図である。It is the schematic diagram which expressed the breast image by the coordinate. 探索開始点付近の拡大図である。It is an enlarged view near a search start point. 胸筋領域から濃度参照除外領域を算出する処理に使用される乳腺重複領域を説明するための***画像の模式図である。It is a schematic diagram of the breast image for demonstrating the mammary gland duplication area | region used for the process which calculates a density | concentration reference exclusion area | region from a pectoral muscle area | region. 胸筋領域から濃度参照除外領域を算出する処理に使用されるリンパ肥大症領域を説明するための***画像の模式図である。It is a schematic diagram of the breast image for demonstrating the lymph hypertrophy region used for the process which calculates a density | concentration reference exclusion area | region from a pectoral muscle area | region. ***画像における異常陰影候補領域の模式図である。It is a schematic diagram of the abnormal shadow candidate area | region in a breast image. 辺縁を楕円で近似した場合の異常陰影候補領域の模式図である。It is a schematic diagram of the abnormal shadow candidate area | region at the time of approximating an edge with an ellipse. 内接円で囲まれた領域の濃度を算出する場合の異常陰影候補領域の模式図である。It is a schematic diagram of the abnormal shadow candidate area | region in the case of calculating the density | concentration of the area | region enclosed with the inscribed circle. 辺縁から一定距離の領域を除いた領域の濃度を算出する場合の異常陰影候補領域の模式図である。It is a schematic diagram of the abnormal shadow candidate area | region in the case of calculating the density | concentration of the area | region except the area | region of the fixed distance from the edge. 異常陰影検出装置に表示される濃度関係表の一例を示す図である。It is a figure which shows an example of the density | concentration relationship table displayed on an abnormal shadow detection apparatus.
　以下、本発明に係る実施の形態について説明する。ただし、本発明は図示例のものに限定されるものではない。 Hereinafter, embodiments according to the present invention will be described. However, the present invention is not limited to the illustrated example.
（異常陰影検出装置１０の構成）
　まず、本実施の形態における構成を説明する。
　図１に、本実施の形態における異常陰影検出装置１０の機能構成例を示す。
　図１に示すように、異常陰影検出装置１０は、ＣＰＵ（Central Processing Unit）１１、Ｉ／Ｆ（InterFace）１２、操作部１３、表示部１４、通信部１５、ＲＡＭ（Random Access Memory）１６、ＲＯＭ（Read Only Memory）１７、プリンタ１８等を備えて構成され、各部はバス１９により接続されて構成されている。 (Configuration of Abnormal Shadow Detection Device 10)
First, the configuration in the present embodiment will be described.
FIG. 1 shows a functional configuration example of the abnormal shadow detection apparatus 10 according to the present embodiment.
As shown in FIG. 1, the abnormal shadow detection apparatus 10 includes a CPU (Central Processing Unit) 11, an I / F (InterFace) 12, an operation unit 13, a display unit 14, a communication unit 15, a RAM (Random Access Memory) 16, A ROM (Read Only Memory) 17, a printer 18, and the like are provided, and each unit is connected by a bus 19.
　ＣＰＵ１１は、ＲＯＭ１７に記憶されているシステムプログラムや各種処理プログラムを読み出してＲＡＭ１６内に展開し、展開されたプログラムとの協働により後述する異常陰影検出処理を始めとする各種処理を実行し、異常陰影検出装置１０の各部の動作を集中制御する。 The CPU 11 reads out system programs and various processing programs stored in the ROM 17 and expands them in the RAM 16, and executes various processes including an abnormal shadow detection process described later in cooperation with the expanded programs. The operation of each part of the shadow detection apparatus 10 is centrally controlled.
　Ｉ／Ｆ１２は、画像生成装置Ｇと接続するためのインターフェイスであり、画像生成装置Ｇにおいて生成された画像データを異常陰影検出装置１０に入力する。 The I / F 12 is an interface for connecting to the image generation apparatus G, and inputs image data generated in the image generation apparatus G to the abnormal shadow detection apparatus 10.
　画像生成装置Ｇは、患者の***を被写体として撮影し、撮影した画像をデジタル変換して、***画像データを生成する装置である。画像生成装置Ｇとしては、例えば、ＣＲ（Computed Radiography）装置、ＦＰＤ（Flat Panel Detector）装置等が適用可能である。
　なお、本実施の形態において、画像生成装置Ｇは、一の患者について***画像データＤを生成し、異常陰影検出装置１０に入力する。 The image generation apparatus G is an apparatus that captures a patient's breast as a subject and digitally converts the captured image to generate breast image data. As the image generation device G, for example, a CR (Computed Radiography) device, an FPD (Flat Panel Detector) device, or the like is applicable.
In the present embodiment, the image generation device G generates breast image data D for one patient and inputs it to the abnormal shadow detection device 10.
　操作部１３は、カーソルキーや数字キー、各種機能キーからなるキーボードを備えて構成され、押下されたキーに対応する操作信号をＣＰＵ１１に出力する。なお、必要に応じてマウスやタッチパネル等のポインティングディバイスを含むこととしてもよい。 The operation unit 13 includes a keyboard including cursor keys, numeric keys, and various function keys, and outputs an operation signal corresponding to the pressed key to the CPU 11. Note that a pointing device such as a mouse or a touch panel may be included as necessary.
　表示部１４は、ＬＣＤ（Liquid Crystal Display）やＣＲＴ（Cathode Ray Tube）等のモニタにより構成され、ＣＰＵ１１から入力される表示信号の指示に従って、***画像等の表示を行う。 The display unit 14 includes a monitor such as an LCD (Liquid Crystal Display) or a CRT (Cathode Ray Tube), and displays a breast image or the like in accordance with an instruction of a display signal input from the CPU 11.
　通信部１５は、ネットワークインターフェイスカード、モデム、ターミナルアダプタ等の通信用インターフェイスにより構成され、通信ネットワーク上の外部機器と各種情報の送受信を行う。例えば、通信部１５を介して画像生成装置Ｇから画像データを受信する構成としてもよいし、通信部１５を介して病院内の画像サーバ等に接続する構成としてもよい。 The communication unit 15 includes a communication interface such as a network interface card, a modem, and a terminal adapter, and transmits / receives various information to / from external devices on the communication network. For example, the image data may be received from the image generation device G via the communication unit 15 or may be connected to an image server in a hospital via the communication unit 15.
　ＲＡＭ１６は、ＣＰＵ１１によって実行される各種プログラムやこれらプログラムによって処理されたデータ等を一時的に記憶するワークエリアを形成する。 The RAM 16 forms a work area for temporarily storing various programs executed by the CPU 11 and data processed by these programs.
　ＲＯＭ１７は、記憶手段として機能し、ＣＰＵ１１で実行される各種プログラムやプログラムにより処理の実行に必要なパラメータ、或いは処理結果等のデータを記憶する。これらの各種プログラムは、読取可能なプログラムコードの形態で格納され、ＣＰＵ１１は、当該プログラムコードに従った動作を逐次実行する。
　なお、上記ＲＯＭ１７以外のその他のコンピュータ読み取り可能な媒体として、ＳＤ（Secure Digital）カードやＵＳＢ（Universal Serial Bus）メモリのようなフラッシュメモリ等の不揮発性メモリ、ＣＤ－ＲＯＭ等の可搬型記録媒体を適用することが可能である。また、本発明に係るプログラムのデータ等の各種データを、搬送波（キャリアウェーブ）に重畳させて通信回線を介して提供することも可能である。 The ROM 17 functions as a storage unit, and stores various programs executed by the CPU 11 and data such as parameters necessary for execution of processing by the programs or processing results. These various programs are stored in the form of readable program codes, and the CPU 11 sequentially executes operations according to the program codes.
Other computer-readable media other than the ROM 17 include a non-volatile memory such as a flash memory such as an SD (Secure Digital) card or a USB (Universal Serial Bus) memory, and a portable recording medium such as a CD-ROM. It is possible to apply. It is also possible to provide various data such as program data according to the present invention via a communication line by superimposing them on a carrier wave.
　プリンタ１８は、ＣＰＵ１１の制御に従って、画像データに基づきフィルム等の記録媒体上に画像を形成し出力する。 The printer 18 forms and outputs an image on a recording medium such as a film based on the image data under the control of the CPU 11.
（異常陰影検出装置１０の動作）
　次に、本実施の形態における動作について説明する。
　異常陰影検出装置１０においては、画像生成装置Ｇから***画像データＤが入力されると、以下に説明する異常陰影検出処理を実行する。
　なお、通常の場合、画像生成装置Ｇにおいては被写体の左右の***が撮影される。つまり、左右二つの***画像データＤがそれぞれ画像生成装置Ｇから入力される。以下に説明する異常陰影検出処理においては、一方の***と同様の異常陰影検出処理が他方の***についても行われるため、説明の便宜上、一方の***画像データＤの異常陰影検出処理についてのみ説明する。 (Operation of Abnormal Shadow Detection Device 10)
Next, the operation in this embodiment will be described.
In the abnormal shadow detection apparatus 10, when the breast image data D is input from the image generation apparatus G, an abnormal shadow detection process described below is executed.
Note that in the normal case, the image generation apparatus G captures the left and right breasts of the subject. That is, left and right breast image data D are input from the image generation device G, respectively. In the abnormal shadow detection process described below, the abnormal shadow detection process similar to that for one breast is performed for the other breast. Therefore, for convenience of explanation, only the abnormal shadow detection process for one breast image data D will be described. .
　図２に、異常陰影検出装置１０において実行される異常陰影検出処理のフローチャートを示す。当該処理は、ＣＰＵ１１とＲＯＭ１７に記憶されているプログラムとの協働によるソフトウエア処理により実現される。なお、本実施の形態における***画像データＤの画素値は濃度値を示す。
　以下、説明する処理はＣＰＵ１１によって実行される。 FIG. 2 shows a flowchart of the abnormal shadow detection process executed in the abnormal shadow detection apparatus 10. This process is realized by a software process in cooperation with the CPU 11 and a program stored in the ROM 17. Note that the pixel value of the breast image data D in the present embodiment indicates a density value.
The processing described below is executed by the CPU 11.
　まず、入力された***画像データＤに含まれる***領域が抽出される（ステップＳ１）。
　図３に、***画像データＤを模式的に示す。ステップＳ１においては、***画像データＤから、Ｘ線が被写体を透過した領域（以下、***領域Ｓａという）と、Ｘ線が被写体を透過しなかった領域（以下、***外領域Ｓｂという）とを区分けする処理が行われる。ステップＳ１における処理によって、ＣＰＵ１１は***領域抽出手段として機能する。 First, a breast region included in the input breast image data D is extracted (step S1).
FIG. 3 schematically shows the breast image data D. In step S1, from the breast image data D, an area where X-rays have passed through the subject (hereinafter referred to as breast area Sa) and an area where X-rays did not pass through the subject (hereinafter referred to as outside breast area Sb). Processing to sort is performed. By the process in step S1, the CPU 11 functions as a breast region extracting unit.
　ステップＳ１において行われる***領域の抽出処理は、公知の方法を用いてよい。例えば以下の（１－１）～（１－３）のようにして抽出される。
　なお、***画像データＤが１２ビットのグレースケールのデータであれば、各画素は０～４０９５の画素値で表現される。先述したように、***画像データＤのうち、***領域は白く撮影される。例えば、***領域Ｓａの画素値は低領域（例えば０～１０００等）の間に集中する。逆に被写体以外の領域、つまりＸ線が被写体を通過しなかった領域は黒く撮影される。例えば、１２ビットのデータの場合、***外領域Ｓｂの画素値は高領域（例えば４０００～４０９５等）の間に集中する。以下の（１－１）～（１－３）では、この低領域と高領域の２値化される傾向に基づいて、***領域Ｓａと***外領域Ｓｂを判別することになる。 A known method may be used for the breast region extraction processing performed in step S1. For example, it is extracted as in the following (1-1) to (1-3).
If the breast image data D is 12-bit grayscale data, each pixel is represented by a pixel value of 0 to 4095. As described above, the breast region of the breast image data D is photographed white. For example, the pixel values of the breast region Sa are concentrated between low regions (for example, 0 to 1000). Conversely, the area other than the subject, that is, the region where the X-rays do not pass through the subject are photographed in black. For example, in the case of 12-bit data, the pixel values of the extramammary region Sb are concentrated between high regions (eg, 4000 to 4095). In the following (1-1) to (1-3), the breast region Sa and the extramammary region Sb are discriminated based on the tendency to binarize the low region and the high region.
　（１－１）***画像データＤの画素の２値化処理
　まず、***画像データＤを一定区画（例えば８ピクセル四方等）ごとにセグメント化（以下、この一定区画の個々を単にセグメントという）する。
　そして、各セグメントの平均画素値が閾値と比較される。この閾値は、***領域Ｓａとみなすための閾値であり、予めＲＯＭ１７に記憶されている。閾値と比較されることにより、各セグメントは２値化される。つまり、閾値よりも小さな画素値を示しているセグメントを「１」、閾値よりも大きな画素値を示しているセグメントを「０」とするよう処理が行われる。
　そして、各セグメントの２値化された値が、形態学に基づいて修正される。具体的には、２値化された値が「０」であるセグメントの周囲のセグメントが全て「１」であれば、このセグメントは***領域Ｓａである可能性が高いため、このセグメントの２値化された値が「１」に修正される等によって修正されることになる。
　以上の処理により、***画像データＤをセグメントごとに２値化することができる。なお、***画像データＤをセグメントに分けず、全ての画素について２値化を行ってもよいが、本実施の形態においては処理負荷軽減のため、以上のようにセグメントごとに２値化を行うこととする。 (1-1) Binarization Processing of Pixels of Breast Image Data D First, the breast image data D is segmented for each predetermined section (for example, 8 pixels square) (hereinafter, each of the fixed sections is simply referred to as a segment). .
Then, the average pixel value of each segment is compared with a threshold value. This threshold value is a threshold value for considering the breast region Sa, and is stored in the ROM 17 in advance. Each segment is binarized by being compared with a threshold value. That is, processing is performed so that a segment indicating a pixel value smaller than the threshold is “1” and a segment indicating a pixel value larger than the threshold is “0”.
Then, the binarized value of each segment is corrected based on the morphology. Specifically, if all the segments around the segment whose binarized value is “0” are all “1”, this segment is likely to be the breast region Sa, so the binary value of this segment The corrected value is corrected, for example, by correcting it to “1”.
Through the above processing, the breast image data D can be binarized for each segment. Note that binarization may be performed for all pixels without dividing the breast image data D into segments, but in this embodiment, binarization is performed for each segment as described above in order to reduce the processing load. I will do it.
　（１－２）***画像データＤを２分割する
　次に、***画像データＤを均等に垂直方向に２分割する。つまり***画像データＤを左右に２分割する。当該２分割された領域のうち、明るい領域（つまり白い画素が多い領域）を判別する。判別方法としては、例えば、この左右２つの領域のうち、２値化された値が「１」を有するセグメントが多い方の領域を明るい領域と判断する等によって行われる。つまり、白い画素が多い方の領域に***が存在すると判別することになる。 (1-2) Dividing the breast image data D into two Next, the breast image data D is equally divided into two in the vertical direction. That is, the breast image data D is divided into left and right parts. Among the two divided areas, a bright area (that is, an area with many white pixels) is determined. As a determination method, for example, an area having a larger number of segments having a binarized value “1” of the two left and right areas is determined as a bright area. That is, it is determined that the breast is present in the region with the larger number of white pixels.
　（１－３）***領域の候補抽出
　次いで、***画像データＤのうち明るい領域の中から、***領域Ｓａの候補が抽出される。具体的には、まず、各セグメントの２値化された値が「１」の領域の広さが判断される。この領域が予め定められた閾値よりも十分に広いと判断された場合には、当該領域を***領域Ｄａの候補とする。予め定められた閾値よりも広くはないと判断された場合には、以下のような調整処理が行われることになる。
　あるセグメント（対象セグメントという）を囲む４つのセグメント（上下左右のセグメント）を関心セグメントとする。対象セグメントの平均画素値が、ＲＯＭ１７に予め定められた閾値（（１－１）で使用した閾値とは異なる値を用いてよい）よりも小さかった場合、関心セグメントには２極化した値のうち「１」がセットされる。逆に、対象セグメントの平均画素値が予め定められた閾値よりも大きければ、関心セグメントには２極化した値のうち「０」がセットされる。このように調整処理を行い、再び２値化された値が「１」の領域の広さが十分に広いか否かを判断する。それでも、***領域Ｓａの候補となる領域が見つからなかった場合には、他方（***画像データＤを２分割した領域のうちの他方。つまり明るくない方の領域を示す。）の領域を探索し、それでも候補が見つからなかった場合には、***画像データＤの全体を探索する。
　このようにして抽出された候補を***領域Ｓａとする。 (1-3) Extraction of Breast Region Candidate Next, a candidate for the breast region Sa is extracted from the bright regions of the breast image data D. Specifically, first, the size of the area where the binarized value of each segment is “1” is determined. If it is determined that this region is sufficiently wider than a predetermined threshold, the region is set as a candidate for the breast region Da. When it is determined that it is not wider than a predetermined threshold value, the following adjustment process is performed.
Four segments (up / down / left / right segments) surrounding a certain segment (referred to as a target segment) are defined as segments of interest. When the average pixel value of the target segment is smaller than the threshold value predetermined in the ROM 17 (a value different from the threshold value used in (1-1) may be used), the segment of interest has a bipolar value. Of these, “1” is set. On the contrary, if the average pixel value of the target segment is larger than a predetermined threshold value, “0” of the bipolar values is set in the segment of interest. The adjustment process is performed in this manner, and it is determined again whether the area of the binarized value “1” is sufficiently wide. If a candidate region for the breast region Sa is still not found, the other region (the other of the regions obtained by dividing the breast image data D into two regions, that is, the region that is not bright) is searched. If no candidate is still found, the whole breast image data D is searched.
The candidate extracted in this way is defined as a breast region Sa.
　図２に戻り、ステップＳ１において算出された***領域Ｓａから、胸筋領域Ｍ１が抽出される（ステップＳ２）。
　ステップＳ２において行われる胸筋領域Ｍ１の抽出処理は、公知の方法を用いてよい。例えば、ステップＳ２において行われる胸筋領域Ｍ１の抽出処理は、以下の（２－１）～（２－５）のようにして抽出される。 Returning to FIG. 2, the pectoral muscle region M1 is extracted from the breast region Sa calculated in step S1 (step S2).
A known method may be used for the extraction processing of the pectoral muscle region M1 performed in step S2. For example, the pectoral muscle region M1 extraction process performed in step S2 is extracted as in the following (2-1) to (2-5).
（２－１）エッジ強調処理
　まず、***画像データＤの各画素を注目画素としてプレヴィット（Prewitt）フィルタ
によりフィルタ処理が施される。これにより、エッジが強調された***画像データＤ（以下区別するために***画像データＤ１という）が取得される。この***画像データＤ１の各画素の画素値は、エッジ強度を示す値となる。 (2-1) Edge Enhancement Processing First, filter processing is performed by a Prewitt filter with each pixel of breast image data D as a target pixel. Thereby, breast image data D (hereinafter referred to as breast image data D1 for distinction) in which the edge is emphasized is acquired. The pixel value of each pixel of the breast image data D1 is a value indicating edge strength.
　以下、図４Ａに示すように、***画像データＤ１における各画素の位置は、***画像データＤ１における***の左右方向をＸ軸、これと垂直方向をＹ軸とした座標（Ｘ，Ｙ）で表す。また、***画像データＤ１における座標（Ｘ，Ｙ）の画素値をＶ（Ｘ、Ｙ）と表す。また、Ｘ軸方向の画像端の座標をＸｍａｘ、Ｙ軸方向の画像端をＹｍａｘとして表す。 Hereinafter, as shown in FIG. 4A, the position of each pixel in the breast image data D1 is represented by coordinates (X, Y) with the left-right direction of the breast in the breast image data D1 as the X axis and the vertical direction as the Y axis. . Further, the pixel value of the coordinates (X, Y) in the breast image data D1 is represented as V (X, Y). Further, the coordinates of the image end in the X-axis direction are represented as X max , and the image end in the Y-axis direction is represented as Y max .
（２－２）スキンラインＳＬの抽出
　次いで、***画像データＤ１における***領域Ｓａと***外領域Ｓｂとの境界点であるスキンラインＳＬを抽出する。ステップＳ１において算出された***領域Ｓａと***外領域Ｓｂとの境界をスキンラインＳＬとしてもよいが、（２－１）においてエッジの強調処理が行われているため、次のようにスキンラインＳＬを抽出する。
　***画像データＤ１の各Ｙ座標（０～Ｙｍａｘ）において、Ｘ軸方向に探索が行われ、Ｖ（Ｘ、Ｙ）が最大となる座標Ｓ（Ｙ）が抽出される。これにより、***画像データＤ１の各Ｙ座標におけるエッジが抽出される。抽出されたエッジは、図４Ａに示すように、***画像データＤ１における***領域Ｓａと***外領域Ｓｂとの境界点であり、スキンラインＳＬを構成する。 (2-2) Extraction of Skin Line SL Next, a skin line SL that is a boundary point between the breast region Sa and the extramammary region Sb in the breast image data D1 is extracted. The boundary between the breast region Sa and the extramammary region Sb calculated in step S1 may be the skin line SL. However, since the edge enhancement processing is performed in (2-1), the skin line SL is as follows. To extract.
For each Y coordinate (0 to Y max ) of the breast image data D1, a search is performed in the X-axis direction, and a coordinate S (Y) that maximizes V (X, Y) is extracted. Thereby, the edge in each Y coordinate of the breast image data D1 is extracted. As shown in FIG. 4A, the extracted edge is a boundary point between the breast region Sa and the extramammary region Sb in the breast image data D1, and constitutes a skin line SL.
（２－３）胸筋ラインＬ探索開始点の決定
　Ｖ（Ｘ，０）が最大となる座標Ｓ（０）（Ｙ座標０の画像端のスキンラインＳＬの位置）より数画素下の座標Ａを開始基準点とする。座標ＡからＸ軸方向に基準点を１画素ずつずらしながら胸筋ラインＬの探索開始点Ｂを探索するための以下の処理が行われる。
　まず、図４Ａに示すように、各基準点をそれぞれ中心として、Ｙ軸方向との角度が０～－３０°の範囲について、１°刻みで***画像データＤ１のＹ軸方向の幅の１／５の長さをもつ探索ラインｌａ０～ｌａ３０が設定される。次いで、探索ラインｌａ０～ｌａ３０上の画素値の平均値がそれぞれ算出される。全ての基準点について、探索ラインｌａ０～ｌａ３０上の画素値の平均値をそれぞれ算出後、算出された平均値が最大となった探索ラインの基準点が胸筋ライン探索開始点Ｂとして決定される。
　このとき、胸筋ライン探索開始点Ｂが画像右端（Ｘｍａｘ）に近い場合、例えば、右端から１０画素までの場合、胸筋領域Ｍ１はなしと判断される。また、算出された最大の平均値が予め定めら得た閾値、例えば、３００より小さい場合は、胸筋領域Ｍ１なしと判断される。胸筋領域Ｍ１なしと判断された場合、例えば、表示部１４にエラーメッセージ等が表示され、異常陰影検出処理は終了する。 (2-3) Determination of the start point of the pectoral muscle line L The coordinate A several pixels below the coordinate S (0) (the position of the skin line SL at the image end of the Y coordinate 0) at which V (X, 0) is maximum Is the starting reference point. The following processing for searching for the search start point B of the pectoral muscle line L is performed while shifting the reference point pixel by pixel from the coordinate A in the X-axis direction.
First, as shown in FIG. 4A, 1 / of the width of the breast image data D1 in the Y-axis direction in increments of 1 ° with respect to each reference point in the range of 0 to −30 ° with respect to the Y-axis direction. Search lines la0 to la30 having a length of 5 are set. Next, the average value of the pixel values on the search lines la0 to la30 is calculated. After calculating the average value of the pixel values on the search lines la0 to la30 for all the reference points, the reference point of the search line having the maximum calculated average value is determined as the pectoral muscle line search start point B. .
At this time, when the pectoral muscle line search start point B is close to the right end (X max ) of the image, for example, from the right end to 10 pixels, it is determined that there is no pectoral muscle region M1. When the calculated maximum average value is smaller than a predetermined threshold value, for example, 300, it is determined that there is no pectoral muscle region M1. When it is determined that there is no pectoral muscle region M1, for example, an error message or the like is displayed on the display unit 14, and the abnormal shadow detection process ends.
（２－４）胸筋領域Ｍ１と***領域Ｓａの境界（胸筋ラインＬ）の探索
　図４Ｂに、胸筋ライン探索開始点Ｂ付近の拡大図を示す。図４Ｂに示すように、胸筋ライン探索開始点Ｂを基点として、Ｙ軸方向との角度±９°の範囲について、１°刻みで***画像データＤのＹ軸方向の幅の１／５の長さをもつ探索ラインｌｂ０～ｌｂ１８が設定される。次いで、探索ラインｌｂ０～ｌｂ１８上の画素値の平均値がそれぞれ算出される。そして、算出された平均値が最大となった探索ラインｌｂｎ（ｎは０～１８のいずれかの整数を示す）が胸筋ラインＬとして決定される。
　次いで、胸筋ライン探索開始点ＢからＹ軸方向の幅の１／１０の地点を基点として、同様の処理が行われる。その後、更に、前回基点とした地点からＹ軸方向の幅の１／１０の地点を基点として、同様の処理が行われる。画像端に到達するまで同様の処理を繰り返すことにより、胸筋ラインＬが抽出される。
　なお、探索ラインｌｂ０～ｌｂ１８の最大の平均値が予め定められた閾値（例えば３００等）より小さい場合は、不明瞭な胸筋ラインＬと判断される。探索ラインｌｂ０～ｌｂ１８の最大の平均値が閾値より大きい基点が２以上ある場合は、それらの基点から２次の近似曲線を引き、この曲線が胸筋ラインＬとして抽出される。探索ラインｌｂ０～ｌｂ１８の最大の平均値が閾値より大きい基点が１以下である場合は、胸筋ライン探索開始点ＢをＹ軸方向に１画素分ずらして上記処理が実行される。 (2-4) Search for Boundary (Pectoral Muscle Line L) Between Pectoral Muscle Region M1 and Breast Region Sa FIG. 4B shows an enlarged view near the pectoral muscle line search start point B. As shown in FIG. 4B, with respect to the range of the angle ± 9 ° with respect to the Y-axis direction with the pectoral muscle line search start point B as the base point, the width of the breast image data D in the Y-axis direction is 1/5 in increments of 1 °. Search lines lb0 to lb18 having a length are set. Next, the average value of the pixel values on the search lines lb0 to lb18 is calculated. Then, the search line lbn (n is an integer from 0 to 18) having the maximum calculated average value is determined as the pectoral muscle line L.
Next, similar processing is performed using a point that is 1/10 of the width in the Y-axis direction from the pectoral muscle line search start point B as a base point. Thereafter, the same processing is performed with a point that is 1/10 of the width in the Y-axis direction from the point set as the previous base point. By repeating the same process until the image end is reached, the pectoral muscle line L is extracted.
When the maximum average value of the search lines lb0 to lb18 is smaller than a predetermined threshold (for example, 300), it is determined that the pectoral muscle line L is unclear. When there are two or more base points where the maximum average value of the search lines lb0 to lb18 is larger than the threshold value, a quadratic approximate curve is drawn from these base points, and this curve is extracted as the pectoral muscle line L. If the base point where the maximum average value of the search lines lb0 to lb18 is greater than the threshold value is 1 or less, the pectoral muscle line search start point B is shifted by one pixel in the Y-axis direction and the above processing is executed.
（２－５）胸筋領域Ｍ１の抽出
　胸筋ラインＬが抽出されると、この抽出された胸筋ラインＬと、***画像領域ＳａにおいてスキンラインＳＬと逆側の画像端に囲まれた領域（図４Ａ及び図４Ｂに粗い点で示す領域）が胸筋領域Ｍ１として抽出される。 (2-5) Extraction of the pectoral muscle region M1 When the pectoral muscle line L is extracted, the extracted pectoral muscle line L and the region surrounded by the image end opposite to the skin line SL in the breast image region Sa (A region indicated by rough points in FIGS. 4A and 4B) is extracted as the pectoral muscle region M1.
　図２に戻り、ステップＳ２において抽出された胸筋領域Ｍ１のうち、濃度を計測しない領域（以下、濃度参照除外領域という）が確定され、除外される（ステップＳ３）。具体的には、ステップＳ２において***領域Ｓａから胸筋領域Ｍ１が抽出されるが、この胸筋領域Ｍ１のうち胸筋ラインＬ付近の領域（以下、乳腺重複領域Ｔ１という）は、胸筋領域Ｍ１の濃度として考慮に入れない。なぜなら、乳腺重複領域Ｔ１は、撮影時のポジショニング不良等のために、乳腺と胸筋が重複して撮影された領域である可能性があるからである。つまり、乳腺重複領域Ｔ１の濃度が胸筋領域Ｍ１の濃度計算に含まれると、筋組織としての正確な濃度の算出ができない恐れがある。
　また、胸筋領域Ｍ１内にリンパ肥大症等の病変部が含まれていた場合にも、この病変部付近の領域（以下、リンパ肥大症領域Ｔ２という）を濃度計算から除外する。リンパ肥大症領域Ｔ２の濃度と正常なリンパ等の組織との濃度は異なるからである。 Returning to FIG. 2, a region where density is not measured (hereinafter referred to as a density reference exclusion region) is determined and excluded from the pectoral muscle region M1 extracted in step S2 (step S3). Specifically, the pectoral muscle region M1 is extracted from the breast region Sa in step S2, and a region in the pectoral muscle region M1 near the pectoral muscle line L (hereinafter referred to as a mammary gland overlap region T1) is a pectoral muscle region. Not taken into account as the concentration of M1. This is because the mammary gland overlap region T1 may be a region in which the mammary gland and the pectoral muscle are imaged due to poor positioning during imaging. That is, if the density of the mammary gland overlap region T1 is included in the density calculation of the pectoral muscle region M1, there is a possibility that the accurate concentration as a muscle tissue cannot be calculated.
In addition, even when a lesion such as lymphoproliferation is included in the pectoral muscle region M1, a region in the vicinity of this lesion (hereinafter referred to as lymphoproliferative region T2) is excluded from the density calculation. This is because the concentration of the lymph hypertrophy region T2 and the concentration of tissues such as normal lymph are different.
　なお、本実施の形態では、濃度参照除外領域として、上述した乳腺重複領域Ｔ１とリンパ肥大症領域Ｔ２を確定する。なお、濃度参照除外領域は、正常な筋組織とみなさない領域であればよく、本実施の形態におけるものに限られない。例えばリンパ肥大症以外の病変部や、ユーザが予め設定した濃度参照除外領域をＲＯＭ１７に保持しておいてもよい。 In the present embodiment, the aforementioned mammary gland overlap region T1 and lymphoproliferative region T2 are determined as the concentration reference exclusion region. The concentration reference exclusion region may be a region that is not regarded as a normal muscle tissue, and is not limited to that in the present embodiment. For example, the ROM 17 may hold a lesion area other than lympho-hypertrophy and a density reference exclusion area preset by the user.
　以下、ステップＳ３における処理について説明する。
　ステップＳ３において行われる乳腺重複領域Ｔ１とリンパ肥大症領域Ｔ２の確定及び除外処理は、以下の（３－１）～（３－３）のようにして算出される。 Hereinafter, the process in step S3 will be described.
The confirmation and exclusion processing of the mammary gland overlap region T1 and the lymphoproliferative region T2 performed in step S3 is calculated as follows (3-1) to (3-3).
　（３－１）乳腺重複領域Ｔ１の確定
　まず、乳腺重複領域Ｔ１の確定について説明する。
　以下、図５に示すように、***画像データＤにおいて、胸筋ラインＬ上にある画素の座標を（Ｘｍａｘ－Ｘｍｕｓｃｌｅ[ｋ]、ｋ）とする。なお、ｋは０～Ｙｍａｘの任意の整数である。
　つまり、胸筋ラインＬのうちＹの値が０の座標（以下、座標Ｐ１という。）は（Ｘｍａｘ－Ｘｍｕｓｃｌｅ[０]、０）である。
　また、Ｘｍｕｓｃｌｅ[ｋ]が０の時のｋの値を、ＹｍｕｓｃｌｅＭＡＸとする。つまり、胸筋ラインＬのうちＸの値がＸｍａｘの座標（以下、座標Ｐ２という。）は（Ｘｍａｘ、ＹｍｕｓｃｌｅＭＡＸ）である。 (3-1) Determination of the mammary gland overlap region T1 First, the determination of the mammary gland overlap region T1 will be described.
Hereinafter, as shown in FIG. 5, in the breast image data D, the coordinates of the pixel on the pectoral muscle line L are (X max −X muscle [k], k). Note that k is an arbitrary integer from 0 to Y max .
In other words, the coordinate of the pectoral muscle line L with the value of Y being 0 (hereinafter referred to as coordinate P1) is (X max -X muscle [0], 0).
Also, the value of k when X muscle [k] is 0 is assumed to be Y muscleMAX . That is, in the pectoral muscle line L, the coordinates of the X value of X max (hereinafter referred to as coordinates P2) are (X max , Y muscleMAX ).
　本実施の形態においては、乳腺重複領域Ｔ１の基準として、座標Ｐ２とＸ座標を同じくして、Ｙ座標の値がＰ２のＹ座標の２／３である座標（以下、座標Ｐ３という。）とＰ１を結んだ直線（図５に破線で示す。以下、直線Ｌ１という。）によって区切られる領域を使用する。つまり胸筋領域Ｍ１のうち、Ｘ座標の値を同じくして直線Ｌ１よりもＹ座標の値が大きい領域を乳腺重複領域Ｔ１とする。なお、乳腺重複領域Ｔ１の確定の方法は、胸筋領域Ｍ１や胸筋ラインＬ等から決定されればよく、これに限られない。 In the present embodiment, as a reference for the mammary gland overlap region T1, the coordinate P2 and the X coordinate are the same, and the value of the Y coordinate is 2/3 of the Y coordinate of P2 (hereinafter referred to as the coordinate P3). An area delimited by a straight line connecting P1 (shown by a broken line in FIG. 5 and hereinafter referred to as a straight line L1) is used. That is, in the pectoral muscle region M1, a region having the same X coordinate value and a larger Y coordinate value than the straight line L1 is defined as a mammary gland overlap region T1. The method for determining the mammary gland overlap region T1 is not limited to this as long as it is determined from the pectoral muscle region M1, the pectoral muscle line L, and the like.
　（３－２）胸筋領域Ｍ１から乳腺重複領域Ｔ１を除外
　次に、胸筋領域Ｍ１から乳腺重複領域Ｔ１を除外する。以下、胸筋領域Ｍ１から乳腺重複領域Ｔ１を除外した領域（以下、胸筋領域Ｍ２という。図５Ａに網掛けで示す領域である。）を具体的に示す。 (3-2) Excluding the mammary gland overlap region T1 from the pectoral muscle region M1 Next, the mammary gland overlap region T1 is excluded from the pectoral muscle region M1. Hereinafter, a region obtained by excluding the mammary gland overlap region T1 from the pectoral muscle region M1 (hereinafter referred to as pectoral muscle region M2, which is a region indicated by shading in FIG. 5A) will be specifically shown.
　まず、直線Ｌ１をＹ＝ａＸ＋ｂと表現する。なお、ａ及びｂは以下の式のようになる。
Figure JPOXMLDOC01-appb-M000001
 First, the straight line L1 is expressed as Y = aX + b. Note that a and b are expressed by the following equations.
Figure JPOXMLDOC01-appb-M000001
　胸筋領域Ｍ１から乳腺重複領域Ｔ１を除外した胸筋領域Ｍ２は、以下の式によって囲まれる領域として抽出されることになる。
Figure JPOXMLDOC01-appb-M000002
 The pectoral muscle region M2 excluding the mammary gland overlap region T1 from the pectoral muscle region M1 is extracted as a region surrounded by the following expression.
Figure JPOXMLDOC01-appb-M000002
　（３－３）リンパ肥大症領域Ｔ２の確定及び除外
　次に、胸筋領域Ｍ２に含まれるリンパ肥大症領域Ｔ２の確定について説明する。
　リンパ肥大症領域Ｔ２の確定及び除外については、公知の方法を用いてよいが、例えば以下のように、曲率を用いた判断を行うことによりリンパ肥大症領域Ｔ２の確定をする方法が挙げられる。 (3-3) Determination and Exclusion of Lymphomegaly Region T2 Next, the determination of the lymphatic hypertrophy region T2 included in the pectoral muscle region M2 will be described.
A known method may be used for determining and excluding the lymph hypertrophy region T2. For example, a method of determining the lymph hypertrophy region T2 by performing a determination using a curvature as described below may be used.
　曲率は、胸筋領域Ｍ２に含まれる画素の座標（Ｘ、Ｙ）及び当該画素の画素値、つまり濃度（Ｚとする）の３方向（Ｘ、Ｙ、Ｚの３軸）の信号成分からなる濃度分布から得られる曲面から、注目画素の法断面を円で近似し、その円の半径を求めることにより算出される。曲率は、曲面が凸形状か凹形状かを示す指標である。つまり、正の方向に曲率が大きいほど曲面は凹形状を示し、負の方向に曲率の値が大きいほど凸形状を示す。したがって、曲率の絶対値が大きければ大きいほど、当該注目画素付近における濃度勾配が大きいことを示す。 The curvature is composed of signal components in three directions (three axes of X, Y, and Z) of the coordinates (X, Y) of the pixel included in the pectoral muscle region M2 and the pixel value of the pixel, that is, the density (Z). It is calculated by approximating the normal section of the pixel of interest with a circle from the curved surface obtained from the density distribution and obtaining the radius of the circle. The curvature is an index indicating whether the curved surface is convex or concave. That is, the larger the curvature in the positive direction, the more concave the curved surface, and the larger the curvature value in the negative direction, the convex shape. Therefore, the larger the absolute value of the curvature, the greater the density gradient in the vicinity of the target pixel.
　リンパ肥大症等の異常陰影は一般的に凹型の形状に分類される。胸筋領域Ｍ２を所定の小領域に分け、当該小領域ごとに曲率の平均値や、曲率の最大値、曲率の最小値等を特徴量として算出する。この特徴量を予め設定された閾値と比較する。閾値以上の特徴量となる小領域、つまり凹形状の大きな領域がリンパ肥大症の候補領域として算出される。
　本実施の形態においては、当該リンパ肥大症の候補領域を円形に近似した時に直径が１０ｍｍ以下であれば、正常なリンパとして認識するものとする。つまり、この近似円の直径が１０ｍｍ以上であった場合に、当該候補領域をリンパ肥大症領域Ｔ２として確定する。ただし、リンパの正常／異常を判断する方法はこれに限られない。
　上記のように算出されたリンパ肥大症領域Ｔ２を胸筋領域Ｍ２から除外する（以下、この領域を濃度参照領域Ｍ３という）。
　図５Ｂに、リンパ肥大症領域Ｔ２と濃度参照領域Ｍ３を示す。図５Ｂに示すように、胸筋領域Ｍ２からリンパ肥大症領域Ｔ２を除外した領域が濃度参照領域Ｍ３となる。
　ステップＳ２及びステップＳ３における処理により、ＣＰＵ１１は領域決定手段として機能する。なお、濃度参照領域Ｍ３は、胸筋領域Ｔ１に基づいて決定されればよく、ステップＳ３において濃度参照除外領域を濃度参照領域Ｍ３から除外する処理は必須ではない。 Abnormal shadows such as lymphatic hypertrophy are generally classified into concave shapes. The pectoral muscle region M2 is divided into predetermined small regions, and an average value of curvature, a maximum value of curvature, a minimum value of curvature, and the like are calculated as feature amounts for each small region. This feature amount is compared with a preset threshold value. A small region having a feature amount equal to or greater than a threshold value, that is, a large concave region is calculated as a candidate region for lymphoproliferation.
In the present embodiment, when the candidate region for lymph hypertrophy is approximated to a circle and the diameter is 10 mm or less, it is recognized as normal lymph. That is, when the diameter of this approximate circle is 10 mm or more, the candidate region is determined as the lymphoproliferative region T2. However, the method of determining normal / abnormal lymph is not limited to this.
The lymphoproliferative region T2 calculated as described above is excluded from the pectoral muscle region M2 (hereinafter, this region is referred to as a concentration reference region M3).
FIG. 5B shows a lymphoproliferative region T2 and a concentration reference region M3. As shown in FIG. 5B, a region obtained by excluding the lymph hypertrophy region T2 from the pectoral muscle region M2 is the concentration reference region M3.
The CPU 11 functions as an area determination unit by the processing in step S2 and step S3. The density reference area M3 may be determined based on the pectoral muscle area T1, and the process of excluding the density reference exclusion area from the density reference area M3 in step S3 is not essential.
　ステップＳ４においては、ステップＳ３において算出された濃度参照領域Ｍ３の平均濃度ＤMuscleAveが算出される（ステップＳ４）。具体的には、濃度参照領域Ｍ３に含まれる画素がカウントされ、濃度参照領域Ｍ３の画素数Ｎが取得される。濃度参照領域Ｍ３に含まれる任意の画素の画素値（濃度）をＤ（ｘ、ｙ）とする。平均濃度ＤMuscleAveは、以下の式のように、濃度参照領域Ｍ３に含まれる画素の画素値の総和を、濃度参照領域Ｍ３に含まれる画素数Ｎで割ることによって算出される。
Figure JPOXMLDOC01-appb-M000003
 In step S4, the average density D MuscleAve of the density reference region M3 calculated in step S3 is calculated (step S4). Specifically, the pixels included in the density reference area M3 are counted, and the number N of pixels in the density reference area M3 is acquired. A pixel value (density) of an arbitrary pixel included in the density reference region M3 is defined as D (x, y). The average density D MuscleAve is calculated by dividing the sum of the pixel values of the pixels included in the density reference area M3 by the number N of pixels included in the density reference area M3 as in the following equation.
Figure JPOXMLDOC01-appb-M000003
　図２に戻り、***領域Ｓａの異常陰影の候補が検出される（ステップＳ５）。具体的には、本実施の形態においては、先述したステップＳ３の（３－３）と同様の手法を用いて、***領域Ｓａに含まれる画素の濃度の曲率を基に、異常陰影の候補が算出される。 Referring back to FIG. 2, a candidate for an abnormal shadow in the breast region Sa is detected (step S5). Specifically, in the present embodiment, using the same method as in (3-3) of step S3 described above, abnormal shadow candidates are obtained based on the curvature of the density of the pixels included in the breast region Sa. Calculated.
　なお、ステップＳ５における異常陰影の候補の検出方法は、他の方法であってもよい。例えば、濃度分布の形状等から異常陰影を検出する特開平１０－９１７５８号公報や、濃度のコントラスト差に基づいて異常陰影を検出する特表平０９－５０８８１５号公報に記載されているような公知の方法を用いてもよい。 Note that another method may be used as the method for detecting abnormal shadow candidates in step S5. For example, as disclosed in Japanese Patent Application Laid-Open No. 10-91758 that detects an abnormal shadow from the density distribution shape and the like, and Japanese Patent Application Laid-Open No. 09-508815 that detects an abnormal shadow based on a contrast difference in density. The method may be used.
　具体的には、特開平１０－９１７５８号公報のように、特に乳癌における特徴的形態の一つである腫瘤陰影を検出するのに有効な手法であるアイリスフィルター処理を本実施の形態のステップＳ５において適用してもよい。例えば、***領域Ｓａのうち腫瘤陰影は周囲の画像部分に比べて濃度値がわずかに低い傾向があるので、この濃度値の分布は概略円形の周縁部から中心部に向かうにしたがって濃度値が低くなるという濃度値の勾配を有している。腫瘤陰影においては局所的な濃度値の勾配が認められ、その勾配線は腫瘤の中心方向に集中する。つまり、濃度分布の形状や大きさ、濃度分布の領域の辺縁の特徴に基づいて異常陰影の候補を検出することができる。
　アイリスフィルターは、この濃度値に代表される画像信号の勾配を勾配ベクトルとして算出し、その勾配ベクトルの集中度を出力するものである。これにより、形状が円形に近い濃度勾配を持つ領域が異常陰影の候補として検出されることになる。ステップＳ５においては、このアイリスフィルター処理によって算出される勾配ベクトルの集中度に基づいて異常陰影を検出するようにしてもよい。 Specifically, as disclosed in Japanese Patent Laid-Open No. 10-91758, the iris filter process, which is an effective technique for detecting a mass shadow that is one of the characteristic forms particularly in breast cancer, is performed in step S5 of the present embodiment. May be applied. For example, in the breast region Sa, the mass shadow tends to have a slightly lower density value than the surrounding image portion. Therefore, the density value distribution decreases in the density value from the substantially circular periphery toward the center. It has a gradient of density value. In the tumor shadow, a local concentration value gradient is observed, and the gradient line is concentrated toward the center of the tumor mass. That is, abnormal shadow candidates can be detected based on the shape and size of the density distribution and the features of the edges of the density distribution area.
The iris filter calculates the gradient of the image signal typified by this density value as a gradient vector and outputs the degree of concentration of the gradient vector. As a result, a region having a density gradient close to a circle is detected as an abnormal shadow candidate. In step S5, an abnormal shadow may be detected based on the concentration degree of the gradient vector calculated by the iris filter process.
　また、特表平０９－５０８８１５号公報のように、濃度のコントラスト差を用いて異常陰影が検出されるようにしてもよい。具体的には、***領域Ｓａを複数グレー・レベル閾値処理、ならびに特異性を高めるための正確な領域増大および特徴分析を行い、異常陰影の候補の検出として、注目画素の放射角を基準にした累積縁勾配配向ヒストグラム分析によって算出される濃度のコントラスト差に基づいて、腫瘤辺縁の周囲か、腫瘤内か、腫瘤の周囲かを判別する等によって異常陰影が検出されるようにしてもよい。
　なお、ステップＳ５における処理によって、ＣＰＵ１１は異常陰影候補検出手段として機能する。 Further, as described in JP-A-09-508815, an abnormal shadow may be detected using a difference in density contrast. Specifically, multiple gray level threshold processing is performed on the breast region Sa, accurate region increase and feature analysis are performed to increase specificity, and an abnormal shadow candidate is detected based on the emission angle of the pixel of interest. An abnormal shadow may be detected by determining whether the periphery of the tumor edge, the inside of the tumor, or the periphery of the tumor based on the contrast difference in density calculated by the cumulative edge gradient orientation histogram analysis.
Note that the CPU 11 functions as an abnormal shadow candidate detecting means by the processing in step S5.
　図６Ａに、ステップＳ５において検出された異常陰影の候補領域を模式的に示す。なお、ステップＳ５によって検出された異常陰影の候補を異常陰影候補領域Ｔ３とする。つまり、異常陰影候補領域Ｔ３に含まれる画素の濃度の曲率は一定の閾値以上であるので、異常陰影の候補としてステップＳ５において検出されることになる。 FIG. 6A schematically shows the abnormal shadow candidate areas detected in step S5. Note that the abnormal shadow candidate detected in step S5 is defined as an abnormal shadow candidate region T3. That is, since the curvature of density of the pixels included in the abnormal shadow candidate region T3 is equal to or greater than a certain threshold value, the abnormal shadow candidate is detected in step S5.
　次いで、ステップＳ５において検出された異常陰影候補領域Ｔ３の平均濃度が算出される（ステップＳ６）。 Next, the average density of the abnormal shadow candidate region T3 detected in step S5 is calculated (step S6).
　以下、ステップＳ６における平均濃度算出の方法について説明する。
　異常陰影候補領域Ｔ３の平均濃度の計測は、例えば以下の２つの方法（６－１）、（６－２）が挙げられる。
　なお、異常陰影候補領域Ｔ３の辺縁付近の領域は、曲率が閾値に近い。つまり、以下説明する２つの方法はいずれも、辺縁付近は異常陰影である可能性が比較的低い領域であるので、当該辺縁付近の領域を除外して平均濃度を計測することを目的としている。 Hereinafter, the method of calculating the average density in step S6 will be described.
For example, the following two methods (6-1) and (6-2) can be used to measure the average density of the abnormal shadow candidate region T3.
Note that the curvature of the region near the edge of the abnormal shadow candidate region T3 is close to the threshold value. In other words, both of the two methods described below are areas where the possibility of abnormal shadows is relatively low in the vicinity of the edge, and therefore the purpose is to measure the average density by excluding the area near the edge. Yes.
　（６－１）短軸を直径とする円内の平均濃度を算出
　図６Ｂに、異常陰影候補領域Ｔ３の辺縁を楕円で近似した（以下、単に近似楕円Ｃ１という）場合を模式的に示す。図６Ｂに示すように、近似楕円Ｃ１は長軸ｒｍａｘ、短軸ｒｍｉｎ、及び中心点Ｏ（ｃ、ｄ）によって特定される。この近似楕円Ｃ１はどのような方法を用いて作成してもよいが、例えば、異常陰影候補領域Ｔ３の辺縁を構成する画素の座標を最小二乗法によって近似する等によって作成される。
　図６Ｃに、近似楕円Ｃ１の中心点Ｏ（ｃ、ｄ）を中心とする半径がｒｍｉｎの円Ｃ２を模式的に示す。例えば、この円Ｃ２によって囲まれる領域を異常陰影候補領域Ｔ３の平均濃度として計測してもよい。具体的には、以下の数式で囲まれた画素の平均画素値を計測することになる。平均画素値は、この円Ｃ２の内部に含まれる画素の画素値Ｄｉｊの総和を画素数で割ることにより算出される。
Figure JPOXMLDOC01-appb-M000004
 (6-1) Calculation of Average Concentration in Circle with Short Axis as Diameter FIG. 6B schematically shows a case where the edge of abnormal shadow candidate region T3 is approximated by an ellipse (hereinafter simply referred to as approximate ellipse C1). . As shown in FIG. 6B, the approximate ellipse C1 is specified by the major axis r max , the minor axis r min , and the center point O (c, d). The approximate ellipse C1 may be created using any method, for example, by approximating the coordinates of the pixels constituting the edge of the abnormal shadow candidate region T3 by the least square method.
FIG. 6C schematically shows a circle C2 having a radius r min centered on the center point O (c, d) of the approximate ellipse C1. For example, the area surrounded by the circle C2 may be measured as the average density of the abnormal shadow candidate area T3. Specifically, the average pixel value of the pixels surrounded by the following formula is measured. The average pixel value is calculated by dividing the sum of the pixel values D ij of the pixels included in the circle C2 by the number of pixels.
Figure JPOXMLDOC01-appb-M000004
　（６－２）辺縁領域を除外した領域の平均濃度を算出
　図６Ｄに、異常陰影候補領域Ｔ３の辺縁付近の領域を除外した領域を模式的に示す。本実施の形態においては、辺縁の座標を（Ｘmarginal、Ｙmarginal）とすると、例えば辺縁の座標を中心点Ｏ（ｃ，ｄ）からｔ（０～１の任意の定数を示す。）倍した座標で囲まれた領域内の平均濃度を算出する。具体的には、座標（ｔ（Ｘmarginal－ｃ）、ｔ（Ｙmarginal－ｄ））で囲まれた領域の平均濃度を算出してもよい。この平均濃度は、（６－１）と同様に平均画素値から算出されることになる。
　以上のように算出された異常陰影候補領域Ｔ３の平均濃度をＤAbnormalAveとする。 (6-2) Calculation of Average Density of Area Excluding Edge Area FIG. 6D schematically shows an area excluding the area near the edge of the abnormal shadow candidate area T3. In this embodiment, when the coordinates of the edge are (X marginal , Y marginal ), for example, the coordinates of the edge are t (shown as an arbitrary constant from 0 to 1) from the center point O (c, d). The average density in the area surrounded by the doubled coordinates is calculated. Specifically, the average density of the region surrounded by the coordinates (t (X marginal −c), t (Y marginal −d)) may be calculated. This average density is calculated from the average pixel value as in (6-1).
The average density of the abnormal shadow candidate area T3 calculated as described above is defined as D AbnormalAve .
　図２に戻り、ステップＳ６において取得された異常陰影候補領域Ｔ３の平均濃度ＤAbnormalAveがＤMuscleAveと比較されることによって、当該異常陰影候補領域Ｔ３が異常陰影であるか否かが最終的に判断される（ステップＳ７）。具体的には、平均濃度ＤAbnormalAveとＤMuscleAveの差の絶対値が予め定められた基準値Ｔｈpickup以上であるか否か等によって判断される。つまり、以下の式を満たしている場合には、ステップＳ７において、当該異常陰影候補領域Ｔ３は異常陰影とは判断しない。なお、ステップＳ７において異常陰影であると判断された異常陰影候補領域Ｔ３は、***画像データＤに重畳させて表示部１４に表示されるようにしてもよい。
Figure JPOXMLDOC01-appb-M000005
 　なお、ステップＳ７における処理により、ＣＰＵ１１は判断手段として機能し、基準値Ｔｈpickupは、異常陰影候補領域Ｔ３が異常陰影であると判断するための基準値である。 Returning to FIG. 2, the average density D AbnormalAve of the abnormal shadow candidate area T3 acquired in step S6 is compared with D MuscleAve , so that it is finally determined whether or not the abnormal shadow candidate area T3 is an abnormal shadow. (Step S7). Specifically, the determination is made based on whether or not the absolute value of the difference between the average concentrations D AbnormalAve and D MuscleAve is greater than or equal to a predetermined reference value Th pickup . That is, if the following expression is satisfied, the abnormal shadow candidate area T3 is not determined to be an abnormal shadow in step S7. The abnormal shadow candidate area T3 determined to be an abnormal shadow in step S7 may be superimposed on the breast image data D and displayed on the display unit 14.
Figure JPOXMLDOC01-appb-M000005
 The CPU 11 functions as a determination unit by the processing in step S7, and the reference value Th pickup is a reference value for determining that the abnormal shadow candidate area T3 is an abnormal shadow.
　ステップＳ７において使用される基準値Ｔｈpickupは先述したように、予めＲＯＭ１７に値が記憶されている。この基準値Ｔｈpickupはどのように決定されてもよいが、例えば、以下のような濃度関係表を表示部１４に表示させることにより、ユーザ（つまり読影医等）の判断によって決定されるようにしてよい。 The reference value Th pickup used in step S7 is previously stored in the ROM 17 as described above. The reference value Th pickup may be determined in any way. For example, by displaying the following density relationship table on the display unit 14, the reference value Th pickup may be determined by the judgment of the user (that is, an interpreting doctor or the like). It's okay.
　図７に、基準値Ｔｈpickupを決定する際に使用される濃度関係表を示す。図７に示すように、胸筋濃度帯、腫瘤濃度帯、高濃度腫瘤濃度帯、乳腺濃度帯、及び脂肪濃度帯が表示部１４に表示される。
　異常陰影検出装置１０において過去に実行された異常陰影検出処理によって検出された異常陰影の候補は、ユーザの判断によって区分けされる。つまり、異常陰影検出処理によって検出された異常陰影の候補には、誤検出された正常組織も含まれる可能性がある。ユーザは表示部１４に表示された異常陰影の候補を視認し、当該候補が腫瘤等の病変部であるか、正常な乳腺等であるか診断する。診断の結果、異常陰影候補の領域を組織毎（例えば、胸筋、腫瘤、高濃度腫瘤、乳腺、及び脂肪）に区分けし、当該領域の平均濃度と組織を対応付けてＲＯＭ１７に記憶する。濃度関係表を表示部１４に表示させる際には、ＲＯＭ１７に記憶された組織毎の平均濃度が参照され、ユーザが区分けした組織毎に、ＲＯＭ１７に記憶されている異常陰影候補の平均濃度と標準偏差が表示される。 FIG. 7 shows a concentration relationship table used when determining the reference value Th pickup . As shown in FIG. 7, the pectoral muscle density band, the mass density band, the high density mass density band, the mammary gland density band, and the fat density band are displayed on the display unit 14.
The abnormal shadow candidates detected by the abnormal shadow detection process executed in the past in the abnormal shadow detection apparatus 10 are classified according to the judgment of the user. That is, the abnormal shadow candidates detected by the abnormal shadow detection process may include erroneously detected normal tissues. The user visually recognizes the candidate for an abnormal shadow displayed on the display unit 14, and diagnoses whether the candidate is a lesion such as a tumor or a normal mammary gland. As a result of the diagnosis, regions of abnormal shadow candidates are divided into tissues (for example, pectoral muscles, tumors, high-density tumors, mammary glands, and fats), and the average density and tissues of the regions are associated with each other and stored in the ROM 17. When the density relationship table is displayed on the display unit 14, the average density for each tissue stored in the ROM 17 is referred to, and the average density of the abnormal shadow candidates stored in the ROM 17 and the standard are stored for each tissue classified by the user. Deviation is displayed.
　例えば、異常陰影検出処理によって異常陰影と検出されたが、ユーザが表示部１６に表示された当該領域を視認し、当該領域は乳腺であると判断した領域は、「乳腺濃度帯」として表される。図７に示す例では、「乳腺濃度帯」は濃度が「１６７５±１８５」付近に集中していることを示す。なお、この「１６７５」は異常陰影として検出されたがユーザによって乳腺と判断された領域の濃度の平均値を示し、「１８５」は標準偏差を示す。他の組織についても同様である。 For example, an area that is detected as an abnormal shadow by the abnormal shadow detection process, but the user visually recognizes the area displayed on the display unit 16 and determines that the area is a mammary gland, is represented as a “breast density band”. The In the example shown in FIG. 7, the “mammary gland concentration band” indicates that the concentration is concentrated in the vicinity of “1675 ± 185”. Note that “1675” indicates an average value of the density of an area detected as an abnormal shadow but determined as a mammary gland by the user, and “185” indicates a standard deviation. The same applies to other organizations.
　ユーザは、この濃度関係表に基づいて、基準値Ｔｈpickupを決定する。当該決定された基準値ＴｈpickupはＲＯＭ１７に記憶され、次に実行される異常陰影検出処理のステップＳ７において使用されることになる。例えば、図７に示す例では、胸筋濃度は「１１４１」付近に分布していて、高濃度腫瘤濃度帯は「１３０５」付近に分布しているから、基準値Ｔｈpickupは、「１３０５－１１４１」である「１６４」を設定する等して判断することができる。
　異常陰影検出処理によって検出された異常陰影の候補がユーザによって最終的に判断された組織と当該領域の濃度に基づいて基準値Ｔｈpickupが算出されればよく、他にも、予め定められた計算式に基づいて異常陰影検出処理が実行される度に、ＣＰＵ１１によって基準値Ｔｈpickupが更新される等のように算出してもよい。 The user determines the reference value Th pickup based on the density relationship table. The determined reference value Th pickup is stored in the ROM 17 and used in step S7 of the abnormal shadow detection process to be executed next. For example, in the example shown in FIG. 7, the pectoral muscle concentration is distributed in the vicinity of “1141”, and the high-concentration mass concentration band is distributed in the vicinity of “1305”, so the reference value Th pickup is “1305-1114”. This can be determined by setting “164” that is “.
The reference value Th pickup may be calculated based on the tissue in which the abnormal shadow candidate detected by the abnormal shadow detection process is finally determined by the user and the density of the region. The CPU 11 may calculate the reference value Th pickup every time the abnormal shadow detection process is executed based on the equation.
　以上のように、本実施の形態における異常陰影検出装置１０によれば、胸筋領域の濃度と異常陰影の候補として検出した領域の濃度とを比較することによって、当該領域が異常陰影であるか否かを判断することができる。つまり、年齢に起因する組織変化の少ない胸筋領域の濃度に基づいて、被写体の異常陰影の候補が異常陰影であるか、誤検出されたものであるかを判断することができ、被写体毎の組織変化の影響の少ない異常陰影検出を行うことができる。 As described above, according to the abnormal shadow detection apparatus 10 in the present embodiment, by comparing the density of the pectoral muscle region with the concentration of the region detected as the abnormal shadow candidate, whether the region is an abnormal shadow or not. It can be determined whether or not. In other words, based on the density of the pectoral muscle region with little tissue change due to age, it is possible to determine whether the subject's abnormal shadow candidate is an abnormal shadow or an erroneously detected one. Abnormal shadow detection with little influence of tissue change can be performed.
　また、本実施の形態においては、胸筋領域のうち濃度参照除外領域を確定し、濃度参照除外領域の濃度を胸筋領域の平均濃度として算出しない。つまり、ポジショニング不良の撮影ミスや病変部等、正常な濃度を示さない領域を除外した胸筋領域の平均濃度を算出するので、異常陰影検出の正確性をより向上させることができる。 In the present embodiment, the density reference exclusion region is determined from the pectoral muscle region, and the concentration of the concentration reference exclusion region is not calculated as the average concentration of the pectoral muscle region. That is, since the average density of the pectoral muscle area excluding the area that does not show normal density, such as an imaging error due to poor positioning or a lesion, is calculated, the accuracy of abnormal shadow detection can be further improved.
　なお、上述した本実施の形態における記述は、本発明に係る好適なの一例であり、これに限定されるものではない。
　例えば、本実施の形態におけるステップＳ７では、胸筋領域の平均濃度と異常陰影候補の平均濃度の差と閾値を比較することによって、異常陰影候補が異常陰影か否かを判断したが、胸筋領域の平均濃度と異常陰影候補の平均濃度によって判断されればよく、判断方法はこれに限られない。例えば、予め定められた計算式を胸筋領域の平均濃度と異常陰影候補の平均濃度が満たすか否かによって判断する等してもよい In addition, the description in this Embodiment mentioned above is a suitable example which concerns on this invention, and is not limited to this.
For example, in step S7 in the present embodiment, the difference between the average density of the pectoral muscle region and the average density of the abnormal shadow candidate is compared with a threshold value to determine whether or not the abnormal shadow candidate is an abnormal shadow. What is necessary is just to judge by the average density of an area | region and the average density of an abnormal shadow candidate, and the judgment method is not restricted to this. For example, a predetermined calculation formula may be determined based on whether or not the average density of the pectoral muscle region and the average density of abnormal shadow candidates are satisfied.
　その他、異常陰影検出装置１０を構成する各装置の細部構成及び細部動作に関しても、本発明の趣旨を逸脱することのない範囲で適宜変更可能である。 In addition, the detailed configuration and detailed operation of each device constituting the abnormal shadow detection device 10 can be changed as appropriate without departing from the spirit of the present invention.
　なお、明細書、請求の範囲、図面及び要約を含む２００８年７月１１日に出願された日本特許出願Ｎｏ．２００８－１８０９７２号の全ての開示は、そのまま本出願の一部に組み込まれる。 It should be noted that the Japanese Patent Application No. 10 filed on July 11, 2008, including the description, claims, drawings and abstract. The entire disclosure of 2008-180972 is incorporated in its entirety into this application.
　医療の分野において、医師による***画像の読影を支援するものとして利用可能性がある。 In the medical field, it may be used as a support for interpretation of breast images by doctors.
符号の説明Explanation of symbols
１０　異常陰影検出装置
１１　ＣＰＵ
１２　Ｉ／Ｆ
１３　操作部
１４　表示部
１５　通信部
１６　ＲＡＭ
１７　ＲＯＭ
１８　プリンタ
Ｇ　画像生成装置 10 Abnormal shadow detection device 11 CPU
12 I / F
13 Operation unit 14 Display unit 15 Communication unit 16 RAM
17 ROM
18 Printer G Image generation device

Claims (18)

  1. 　***画像における***領域を抽出する***領域抽出手段と、
    　前記***領域抽出手段によって抽出された***領域における胸筋領域を抽出し、当該抽出された胸筋領域のうち濃度を参照する領域を決定する領域決定手段と、
    　前記***領域抽出手段によって抽出された***領域の濃度に基づいて異常陰影の候補領域を検出する異常陰影候補検出手段と、
    　前記異常陰影候補領域検出手段によって検出された候補領域の平均濃度と、前記領域決定手段によって決定された領域の平均濃度との濃度差を算出し、当該算出された濃度差に基づいて前記候補領域を異常陰影であるか否かを判断する判断手段と、
    　を備える異常陰影検出装置。     Breast area extraction means for extracting a breast area in a breast image;
    A region determination unit that extracts a pectoral muscle region in the breast region extracted by the breast region extraction unit and determines a region of the extracted pectoral muscle region that refers to a concentration;
    An abnormal shadow candidate detecting means for detecting a candidate area of an abnormal shadow based on the density of the breast area extracted by the breast area extracting means;
    A density difference between the average density of the candidate area detected by the abnormal shadow candidate area detection means and the average density of the area determined by the area determination means is calculated, and the candidate area is calculated based on the calculated density difference. Determining means for determining whether or not an abnormal shadow,
    An abnormal shadow detection apparatus comprising:
  2. 　前記判断手段が前記異常陰影候補領域検出手段によって検出された候補領域を異常陰影であるか否かを判断するための基準値を記憶する記憶手段を更に備え、
    　前記判断手段は、前記濃度差の絶対値が前記記憶手段に記憶された基準値以上である場合に、前記候補領域を異常陰影であると判断する請求項１に記載の異常陰影検出装置。     A storage means for storing a reference value for determining whether or not the candidate area detected by the abnormal shadow candidate area detection means is an abnormal shadow;
    The abnormal shadow detection apparatus according to claim 1, wherein the determination unit determines that the candidate area is an abnormal shadow when an absolute value of the density difference is equal to or greater than a reference value stored in the storage unit.
  3. 　前記異常陰影候補検出手段は、前記***領域抽出手段によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度分布から得られる曲率に基づいて異常陰影の候補領域を検出する請求項１又は２に記載の異常陰影検出装置。 The abnormal shadow candidate detection unit detects an abnormal shadow candidate region based on a curvature obtained from a density distribution of neighboring pixels within a predetermined region from an arbitrary pixel of interest in the breast region extracted by the breast region extraction unit. The abnormal shadow detection apparatus according to claim 1 or 2.
  4. 　前記異常陰影候補検出手段は、前記***領域抽出手段によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度差から得られるコントラストに基づいて異常陰影の候補領域を検出する請求項１又は２に記載の異常陰影検出装置。 The abnormal shadow candidate detection means detects an abnormal shadow candidate area based on a contrast obtained from a density difference between neighboring pixels within a predetermined area from an arbitrary target pixel in the breast area extracted by the breast area extraction means. The abnormal shadow detection apparatus according to claim 1 or 2.
  5. 　前記異常陰影候補検出手段は、前記***領域抽出手段によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度、濃度分布の形状、濃度分布の大きさ、濃度分布の領域の辺縁のいずれか一つまたは複数の特徴に基づいて異常陰影の候補領域を検出する請求項１又は２に記載の異常陰影検出装置。 The abnormal shadow candidate detection means includes a density of a neighboring pixel, a density distribution shape, a density distribution size, and a density distribution area within a predetermined area from any target pixel in the breast area extracted by the breast area extraction means. The abnormal shadow detection apparatus according to claim 1, wherein a candidate area for an abnormal shadow is detected based on any one or a plurality of features of the edges.
  6. 　前記領域決定手段は、前記***領域抽出手段によって抽出された***領域における胸筋領域を抽出し、当該抽出された胸筋領域のうち***と胸筋が重なって撮影がなされた領域及び／又は病変部の領域を除外して濃度を参照する領域を決定する請求項１～５のいずれか一項に記載の異常陰影検出装置。 The region determination unit extracts a pectoral muscle region in the breast region extracted by the breast region extraction unit, and among the extracted pectoral muscle region, a region and / or a lesion that has been imaged by overlapping the breast and pectoral muscles The abnormal shadow detection apparatus according to any one of claims 1 to 5, wherein a region for which density is referred to is determined by excluding the region of the portion.
  7. 　***画像における***領域を抽出する***領域抽出工程と、
    　前記***領域抽出工程によって抽出された***領域における胸筋領域を抽出し、当該抽出された胸筋領域のうち濃度を参照する領域を決定する領域決定工程と、
    　前記***領域抽出工程によって抽出された***領域の濃度に基づいて異常陰影の候補領域を検出する異常陰影候補検出工程と、
    　前記異常陰影候補領域検出工程によって検出された候補領域の平均濃度と、前記領域決定工程によって決定された領域の平均濃度との濃度差を算出し、当該算出された濃度差に基づいて前記候補領域を異常陰影であるか否かを判断する判断工程と、
    　を有する異常陰影検出方法。     A breast region extraction step of extracting a breast region in a breast image;
    A region determination step of extracting a pectoral muscle region in the breast region extracted by the breast region extraction step, and determining a region of the extracted pectoral muscle region that refers to a concentration;
    An abnormal shadow candidate detection step of detecting a candidate region of an abnormal shadow based on the density of the breast region extracted by the breast region extraction step;
    A density difference between the average density of the candidate area detected by the abnormal shadow candidate area detection step and the average density of the area determined by the area determination step is calculated, and the candidate area is calculated based on the calculated density difference A determination step of determining whether or not the image is an abnormal shadow,
    An abnormal shadow detection method comprising:
  8. 　前記判断工程は、前記濃度差の絶対値が記憶手段に記憶された基準値以上である場合に、前記候補領域を異常陰影であると判断する請求項７に記載の異常陰影検出方法。 The abnormal shadow detection method according to claim 7, wherein the determination step determines that the candidate area is an abnormal shadow when the absolute value of the density difference is equal to or greater than a reference value stored in a storage unit.
  9. 　前記異常陰影候補検出工程は、前記***領域抽出工程によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度分布から得られる曲率に基づいて異常陰影の候補領域を検出する請求項７又は８に記載の異常陰影検出方法。 The abnormal shadow candidate detection step detects an abnormal shadow candidate region based on a curvature obtained from a density distribution of neighboring pixels within a predetermined region from an arbitrary pixel of interest in the breast region extracted by the breast region extraction step. The abnormal shadow detection method according to claim 7 or 8.
  10. 　前記異常陰影候補検出工程は、前記***領域抽出工程によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度差から得られるコントラストに基づいて異常陰影の候補領域を検出する請求項７又は８に記載の異常陰影検出方法。 The abnormal shadow candidate detecting step detects an abnormal shadow candidate region based on a contrast obtained from a density difference between neighboring pixels within a predetermined region from an arbitrary pixel of interest in the breast region extracted by the breast region extracting step. The abnormal shadow detection method according to claim 7 or 8.
  11. 　前記異常陰影候補検出工程は、前記***領域抽出工程によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度、濃度分布の形状、濃度分布の大きさ、濃度分布の領域の辺縁のいずれか一つまたは複数の特徴に基づいて異常陰影の候補領域を検出する請求項７又は８に記載の異常陰影検出方法。 In the abnormal shadow candidate detection step, the density of neighboring pixels, the shape of the density distribution, the size of the density distribution, the region of the density distribution in the predetermined region range from any target pixel in the breast region extracted by the breast region extraction step The abnormal shadow detection method according to claim 7 or 8, wherein a candidate area for an abnormal shadow is detected based on any one or a plurality of features of the edges of the edge.
  12. 　前記領域決定工程は、前記***領域抽出工程によって抽出された***領域における胸筋領域を抽出し、当該抽出された胸筋領域のうち***と胸筋が重なって撮影がなされた領域及び／又は病変部の領域を除外して濃度を参照する領域を決定する請求項７～１１のいずれか一項に記載の異常陰影検出方法。 The region determination step extracts a pectoral muscle region in the breast region extracted by the breast region extraction step, and a region and / or a lesion in which the breast and pectoral muscles are imaged in the extracted pectoral muscle region. The abnormal shadow detection method according to any one of claims 7 to 11, wherein a region for which density is referred to is determined by excluding the region of the portion.
  13. 　コンピュータを、
    　***画像における***領域を抽出する***領域抽出手段、
    　前記***領域抽出手段によって抽出された***領域における胸筋領域を抽出し、当該抽出された胸筋領域のうち濃度を参照する領域を決定する領域決定手段、
    　前記***領域抽出手段によって抽出された***領域の濃度に基づいて異常陰影の候補領域を検出する異常陰影候補検出手段、
    　前記異常陰影候補領域検出手段によって検出された候補領域の平均濃度と、前記領域決定手段によって決定された領域の平均濃度との濃度差を算出し、当該算出された濃度差に基づいて前記候補領域を異常陰影であるか否かを判断する判断手段、
    　として機能させるためのプログラム。     Computer
    A breast region extraction means for extracting a breast region in a breast image;
    A region determination unit that extracts a pectoral muscle region in the breast region extracted by the breast region extraction unit, and determines a region whose density is referred to from the extracted pectoral muscle region;
    An abnormal shadow candidate detecting means for detecting a candidate area of an abnormal shadow based on the density of the breast area extracted by the breast area extracting means;
    A density difference between the average density of the candidate area detected by the abnormal shadow candidate area detection means and the average density of the area determined by the area determination means is calculated, and the candidate area is calculated based on the calculated density difference. Means for judging whether or not an abnormal shadow,
    Program to function as.
  14. 　前記コンピュータを、
    　前記判断手段が前記異常陰影候補領域検出手段によって検出された候補領域を異常陰影であるか否かを判断するための基準値を記憶する記憶手段として更に機能させ、
    　前記判断手段は、前記濃度差の絶対値が前記記憶手段に記憶された基準値以上である場合に、前記候補領域を異常陰影であると判断する請求項１３に記載のプログラム。     The computer,
    The determination means further functions as a storage means for storing a reference value for determining whether or not the candidate area detected by the abnormal shadow candidate area detection means is an abnormal shadow,
    The program according to claim 13, wherein the determination unit determines that the candidate area is an abnormal shadow when the absolute value of the density difference is equal to or greater than a reference value stored in the storage unit.
  15. 　前記異常陰影候補検出手段は、前記***領域抽出手段によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度分布から得られる曲率に基づいて異常陰影の候補領域を検出する請求項１３又は１４に記載のプログラム。 The abnormal shadow candidate detection unit detects an abnormal shadow candidate region based on a curvature obtained from a density distribution of neighboring pixels within a predetermined region from an arbitrary pixel of interest in the breast region extracted by the breast region extraction unit. The program according to claim 13 or 14.
  16. 　前記異常陰影候補検出手段は、前記***領域抽出手段によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度差から得られるコントラストに基づいて異常陰影の候補領域を検出する請求項１３又は１４に記載のプログラム。 The abnormal shadow candidate detection means detects an abnormal shadow candidate area based on a contrast obtained from a density difference between neighboring pixels within a predetermined area from an arbitrary target pixel in the breast area extracted by the breast area extraction means. The program according to claim 13 or 14.
  17. 　前記異常陰影候補検出手段は、前記***領域抽出手段によって抽出された***領域における任意の注目画素から所定領域範囲内における近傍画素の濃度、濃度分布の形状、濃度分布の大きさ、濃度分布の領域の辺縁のいずれか一つまたは複数の特徴に基づいて異常陰影の候補領域を検出する請求項１３又は１４に記載のプログラム。 The abnormal shadow candidate detection means includes a density of a neighboring pixel, a density distribution shape, a density distribution size, and a density distribution area within a predetermined area from any target pixel in the breast area extracted by the breast area extraction means. The program according to claim 13 or 14, wherein a candidate region for an abnormal shadow is detected based on any one or a plurality of features of the edges of the image.
  18. 　前記領域決定手段は、前記***領域抽出手段によって抽出された***領域における胸筋領域を抽出し、当該抽出された胸筋領域のうち***と胸筋が重なって撮影がなされた領域及び／又は病変部の領域を除外して濃度を参照する領域を決定する請求項１３～１７のいずれか一項に記載のプログラム。 The region determination unit extracts a pectoral muscle region in the breast region extracted by the breast region extraction unit, and among the extracted pectoral muscle region, a region and / or a lesion that has been imaged by overlapping the breast and pectoral muscles The program according to any one of claims 13 to 17, wherein a region for which density is referred to is determined by excluding a portion of the region.
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WO2012072974A3 (en) * 2010-11-30 2012-07-26 Matakina Technology Ltd An imaging technique and imaging system

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JP2006340835A (en) * 2005-06-08 2006-12-21 Konica Minolta Medical & Graphic Inc Displaying method for abnormal shadow candidate, and medical image processing system

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JP2006340835A (en) * 2005-06-08 2006-12-21 Konica Minolta Medical & Graphic Inc Displaying method for abnormal shadow candidate, and medical image processing system

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012072974A3 (en) * 2010-11-30 2012-07-26 Matakina Technology Ltd An imaging technique and imaging system

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