WO2021253732A1 - 医学图像的处理方法、装置、计算机设备和存储介质 - Google Patents

医学图像的处理方法、装置、计算机设备和存储介质 Download PDF

Info

Publication number
WO2021253732A1
WO2021253732A1 PCT/CN2020/133036 CN2020133036W WO2021253732A1 WO 2021253732 A1 WO2021253732 A1 WO 2021253732A1 CN 2020133036 W CN2020133036 W CN 2020133036W WO 2021253732 A1 WO2021253732 A1 WO 2021253732A1
Authority
WO
WIPO (PCT)
Prior art keywords
image
medical
area
medical image
original
Prior art date
Application number
PCT/CN2020/133036
Other languages
English (en)
French (fr)
Inventor
沈嘉浩
宋珂
Original Assignee
飞依诺科技(苏州)有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 飞依诺科技(苏州)有限公司 filed Critical 飞依诺科技(苏州)有限公司
Publication of WO2021253732A1 publication Critical patent/WO2021253732A1/zh

Links

Images

Classifications

    • 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
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10132—Ultrasound image
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G06T2207/20132—Image cropping
    • 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
    • G06T2207/30048—Heart; Cardiac
    • 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
    • G06T2207/30056—Liver; Hepatic

Definitions

  • This application relates to the technical field of medical equipment, and in particular to a processing method, device, computer equipment, and storage medium for medical images.
  • DICOM Digital Imaging and Communications in Medicine
  • DICOM medical images are characterized by high image resolution and high image quality. But at the same time, the amount of data is huge, and there will be some difficulties in its archiving, transmission and management. Therefore, in many cases, DICOM medical images are saved as compressed images with extraneous information, and the saved medical images contain medical images and sensitive information that is not a medical image (such as patient number, examination time) , Check location and other personal privacy information).
  • a method for processing medical images includes:
  • the binarized image of the original medical image includes a plurality of connected regions
  • the obtaining the local entropy grayscale image of the original medical image includes:
  • the gray-scale image of the original medical image is traversed by using a conversion matrix of a preset size to obtain the gray-scale image of the local entropy of the original medical image.
  • the traversing the grayscale image of the original medical image by using a conversion matrix of a preset size to obtain the local entropy grayscale image of the original medical image includes:
  • the local entropy matrix is normalized to obtain the local entropy grayscale image of the original medical image.
  • the performing image segmentation on the local entropy grayscale image to obtain the binarized image of the original medical image includes:
  • An open operation is performed on the binarized image of the local entropy grayscale image to obtain the binarized image of the original medical image.
  • the determining the connected area corresponding to the medical ontology area in the plurality of connected areas according to the area of each of the connected areas includes:
  • the connected area with the largest area is determined as the connected area corresponding to the medical ontology area.
  • the connected area is provided with corresponding corner coordinates; the extraction of the medical image of the medical ontology area from the original medical image according to the connected area corresponding to the medical ontology area includes :
  • the original medical image is cropped according to the corner coordinates of the connected region corresponding to the medical body region to obtain the medical image of the medical body region.
  • the original medical image is an ultrasound image including sensitive information
  • the medical image of the medical body region is an ultrasound image generated when an ultrasound probe scans a target part.
  • a medical image processing device includes:
  • the first acquisition module is used to acquire the local entropy grayscale image of the original medical image, the original medical image including the medical ontology area;
  • An image segmentation module configured to perform image segmentation on the local entropy grayscale image to obtain a binarized image of the original medical image; the binarized image of the original medical image includes a plurality of connected regions;
  • An area determining module configured to determine the connected area corresponding to the medical ontology area among the multiple connected areas according to the area of each of the connected areas;
  • the image extraction module is used to extract the image of the medical ontology area from the original medical image according to the connected area corresponding to the medical ontology area.
  • a computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the medical image processing method described in any one of the foregoing when the computer program is executed.
  • the above-mentioned medical image processing method, device, computer equipment and storage medium obtain the local entropy gray image of the original medical image, the original medical image includes the medical ontology area; perform image segmentation on the local entropy gray image to obtain The binarized image of the original medical image, and determine multiple connected regions in the binarized image; thereby determining the connected region corresponding to the medical ontology region according to the area of each connected region; further, due to the binary If the chemical image corresponds to the original medical image, then according to the connected area corresponding to the medical ontology area, the medical image of the medical ontology area can be accurately and completely extracted from the original medical image, and sensitive information in the medical image can be accurately and completely extracted. Desensitization.
  • FIG. 1 is a schematic flowchart of a method for processing medical images in an embodiment
  • Fig. 2a is a schematic flowchart of step S102 in an embodiment
  • Figure 2b is a schematic diagram of a flow of local entropy extraction in an embodiment
  • Fig. 4 is a schematic diagram of a flow of local entropy extraction in another embodiment
  • FIG. 3 is a schematic flowchart of step S206 in an embodiment
  • Fig. 4a is a schematic flowchart of step S104 in an embodiment
  • Figure 4b is a schematic view of the process of the corrosion operation in an embodiment
  • Figure 4c is a schematic flow chart of an expansion operation in an embodiment
  • Figure 5a is a schematic flowchart of an ultrasound image processing method in an embodiment
  • 5b to 5c are schematic diagrams of original ultrasound images in an embodiment
  • Figure 5d is a schematic diagram of a local entropy grayscale image in an embodiment
  • Figure 5e is a schematic diagram of a binarized image in an embodiment
  • Figure 5f is a schematic diagram of a binarized image after an open operation in an embodiment
  • Figure 5g is a schematic diagram of the ultrasound body region (the largest connected region) in an embodiment
  • 5h to 5i are schematic diagrams of ultrasound body images extracted in an embodiment
  • Figure 6 is a structural block diagram of a medical image processing device in an embodiment
  • Fig. 7 is an internal structure diagram of a computer device in an embodiment.
  • a method for processing medical images includes the following steps:
  • S102 Obtain a local entropy grayscale image of the original medical image, where the original medical image includes the medical ontology region.
  • the original medical image is a medical image image obtained through medical imaging technologies such as computed tomography (CT), nuclear magnetic resonance (MRI), ultrasound imaging (US), digital silhouette angiography (DSA), etc., which include medical ontology images And sensitive information that is not part of the medical ontology image.
  • the medical ontology image is an image obtained by scanning a part of interest.
  • the medical ontology area is a partial area in the original medical image corresponding to the patient's area of interest.
  • the imaging device After obtaining the medical ontology image corresponding to the part of interest, the imaging device generates and displays sensitive information based on personal privacy information such as the patient number, examination time, and examination location to generate the original medical image.
  • the part of interest can be body organs such as the uterus and the heart.
  • the original medical image may be an image saved in the form of a DICOM (Digital imaging and Communications in Medicine, medical imaging imaging and communication standard) medical image compressed and with extraneous information.
  • the format of the original medical image may be
  • the local entropy gray-scale image of the original medical image is obtained from a computer locally or a computer device connected in communication with the computer.
  • the local entropy gray-scale image refers to the gray-scale image obtained by performing local entropy calculation on the gray-scale image corresponding to the original medical image and updating the gray value of each pixel in the original medical image.
  • the local entropy refers to the entropy calculated within the limited N*N (such as 5*5) range of the digital image.
  • the entropy of a digital image is a special statistical form, which reflects the amount of information contained in the aggregation features of the gray-scale distribution in the image.
  • the entropy expression of a gray-scale digital image is:
  • i is the gray value of the pixel
  • p i is the probability that the pixel value i appears in the entire digital image.
  • S104 Perform image segmentation on the local entropy grayscale image to obtain a binarized image of the original medical image.
  • image segmentation refers to the technology and process of dividing an image into several specific regions with unique properties and extracting regions of interest or target parts.
  • the medical ontology image is extracted from the original medical image.
  • the gray value of each pixel in the binarized image is 0 or 255, and the entire image presents an obvious black and white effect.
  • Connected components generally refer to an image area composed of adjacent pixels that have the same pixel value.
  • the binarized image of the original medical image includes multiple connected regions. The area of each connected area may be different or equal.
  • the segmentation threshold is set according to the gray value of each pixel in the local entropy gray image, and the gray value of each pixel in the local entropy gray image is updated, such as updating If it is 0 or 255, it realizes image segmentation of the local entropy grayscale image of the original medical image to obtain the binarized image of the original medical image.
  • the connected area in the binarized image is determined according to the gray value of each pixel in the binarized image.
  • the original medical image contains the medical ontology area corresponding to the medical ontology image and the sensitive area corresponding to the sensitive information.
  • the medical body area and the sensitive area respectively correspond to their own connected areas. Since the medical ontology area and the sensitive area have areas of different sizes in the original medical image, the connected area corresponding to the medical ontology area can be determined from the multiple connected areas of the binarized image according to the area of the connected area.
  • the connected area corresponding to the medical ontology area is determined among the multiple connected areas of the binarized image, thereby determining the position or distribution of the medical ontology area in the binarized image. Because the binarized image corresponds to the original medical image, the position or distribution of the medical ontology area in the original medical image can be known, so that the medical image of the medical ontology area can be extracted from the original medical image.
  • the original medical image includes the medical ontology area; performing image segmentation on the local entropy gray image to obtain the binary image of the original medical image, and determine Multiple connected regions in the binarized image; thereby determining the connected region corresponding to the medical ontology region according to the area of each connected region; further, since the binarized image corresponds to the original medical image, the connected region corresponding to the medical ontology region , It can accurately and completely extract the medical image of the medical ontology area from the original medical image, and desensitize the sensitive information in the medical image. Further, the extracted medical image contains complete medical image information, which solves the technical problem of the lack of medical image information in the traditional technology.
  • step S102 obtaining the local entropy grayscale image of the original medical image includes the following steps:
  • S204 Perform grayscale processing on the original medical image to obtain a grayscale image of the original medical image
  • S206 Traverse the grayscale image of the original medical image by using the conversion matrix of the preset size to obtain the local entropy grayscale image of the original medical image.
  • the conversion matrix is a N*N matrix with a preset size, which is used to delineate the range of N*N in the grayscale image of the original medical image, and calculate the local entropy value in the range of N*N.
  • the original medical image is obtained from the computer locally or from a computer device that is in communication with the computer.
  • the original medical image may have colors.
  • the original medical image is gray-scale processed to obtain the gray-scale image of the original medical image.
  • the local entropy of the original medical image is extracted according to the pixel value of each pixel within the range of the 5*5 conversion matrix, thereby using the 5*5 conversion matrix Traverse the gray-scale image of the original medical image to obtain the local entropy gray-scale image of the original medical image.
  • the grayscale image of the original medical image is obtained by performing grayscale processing on the original medical image; and the grayscale image of the original medical image is traversed using a conversion matrix of a preset size to obtain the local entropy grayscale of the original medical image
  • the image can highlight the medical ontology area and sensitive area in the original medical image, which provides a basis for the subsequent extraction of the medical ontology image.
  • step S206 the gray-scale image of the original medical image is traversed by using a conversion matrix of a preset size to obtain the gray-scale image of local entropy of the original medical image, which includes:
  • the pixel value of each pixel within the range of the N*N conversion matrix is calculated, and the conversion matrix is obtained within the range of N*N.
  • the local entropy value Use the local entropy in the N*N range to update the gray value of the pixel at the center of the N*N matrix, and so on, use the N*N conversion matrix to traverse the gray image of the original medical image to obtain the local entropy matrix. Further, normalize the region entropy matrix to a preset gray value such as (0 value 255), and set it to be eight bits deep for each pixel, that is, a single-channel, eight-bit deep region is obtained Entropy grayscale image. It is understandable that the size of N is an empirical value and can be selected according to actual conditions.
  • the conversion matrix is used to traverse the grayscale image of the original medical image, and the local entropy of each pixel corresponding to the conversion matrix in the grayscale image is calculated; the grayscale of the pixel corresponding to the center position of the conversion matrix in the grayscale image is calculated The value is updated to the calculated local entropy value to obtain the local entropy matrix; the local entropy matrix is normalized to obtain the local entropy grayscale image of the original medical image.
  • the medical ontology area and sensitive area can be further highlighted in the original medical image to ensure the accuracy and completeness of the extraction of the medical ontology image.
  • step S104 image segmentation is performed on the local entropy grayscale image to obtain a binary image of the original medical image, including:
  • S402 Perform image threshold segmentation on the local entropy gray image to obtain a binarized image of the local entropy gray image;
  • S404 Perform an open operation on the binarized image of the local entropy grayscale image to obtain the binarized image of the original medical image.
  • the image threshold segmentation can use the Otsu method (OTSU method).
  • Otsu method Otsu method
  • the Dajin Law is well known to the art staff, so I won’t repeat it here.
  • the open operation includes the process of first corroding and then expanding the image, which is an operation performed on the binarized image to change the size, thickness, and adhesion of the image, thereby optimizing the outline of the binarized image.
  • the corrosion operation is to corrode the edges of the object.
  • the specific operation method is that the M*N rectangle is used as a template to process each pixel in the image as follows: pixel x is at the center of the template, traverse all other pixels covered by the template according to the size of the template, and modify the value of pixel x to all The smallest value in a pixel.
  • the result of this operation is to corrode the prominent points on the periphery of the image.
  • the expansion operation expands the outline of the image.
  • the operation method is similar to the corrosion operation.
  • the M*N rectangle is used as a template to traverse each pixel of the image. The difference is that the value of the modified pixel is not the smallest value among all pixels, but the largest value. The result of this operation will connect and extend the prominent points on the periphery of the image.
  • the segmentation threshold is set according to the gray value of each pixel in the local entropy gray image, and the gray value of each pixel in the local entropy gray image is updated, such as updating If it is 0 or 255, it realizes the image threshold segmentation of the local entropy grayscale image of the original medical image to obtain the binarized image of the original medical image. Further, in order to optimize the contours in the binarized image, the binarized image of the local entropy grayscale image is first eroded and then expanded to obtain the binarized image of the original medical image, which is based on each of the binarized images. The gray value of the pixel determines the connected area in the binarized image.
  • the binarized image of the local entropy gray image is obtained, which realizes the prominent display of the medical ontology area and the sensitive area in the original medical image, and further controls
  • the binarized image of the local entropy grayscale image performs the open operation to obtain the binarized image of the original medical image, optimize the contour in the binarized image, and ensure the accuracy of the connected region, so that the original medical image can be accurately and completely obtained from the original medical image.
  • the medical image of the medical ontology area is extracted from the image, and the sensitive information in the medical image is desensitized.
  • determining the connected area corresponding to the medical ontology area among the multiple connected areas includes: among the multiple connected areas, determining the connected area with the largest area as the connected area corresponding to the medical ontology area Connected areas.
  • the medical ontology area is the area with the largest area in the original medical image. Therefore, among the multiple connected regions, the connected region with the largest area is determined as the connected region corresponding to the medical ontology region.
  • the connected area is provided with corresponding corner coordinates.
  • extracting the medical image of the medical ontology area from the original medical image includes: cutting the original medical image according to the corner coordinates of the connected area corresponding to the medical ontology area to obtain the medicine of the medical ontology area image.
  • the corner points are the vertices of the polygonal shape of the connected region.
  • the connected area has corner coordinates in the binarized image of the original medical image, and the binarized image corresponds to the original medical image. It can be seen that the position of the medical ontology area in the original medical image is explained by taking the connected area of the quadrilateral as an example.
  • Each pixel in the area corresponds to a coordinate value, from which the maximum x-axis coordinate value x1 and the minimum x-axis coordinate value x2 are respectively obtained, and the maximum y-axis coordinate value y1 and the minimum y-axis coordinate value y2 are obtained.
  • the corner coordinates are (x1, y1), (x1, y2), (x2, y1), (x2, y2).
  • the position of the medical ontology area in the original medical image is determined, so that the original medical image is cropped according to the position of the medical ontology area in the original medical image to obtain the medical ontology Medical image of the area.
  • the shape of the connected area is a rectangle
  • the corner coordinates are the coordinates of the four vertices of the rectangle
  • the area within the rectangular frame is the medical ontology area
  • the medical ontology is extracted from the original medical image based on the four vertices of the rectangle. Medical image of the area.
  • the original medical image is an ultrasound image including sensitive information
  • the medical image of the medical body region is an ultrasound image generated when the ultrasound probe scans the target part.
  • the ultrasound image is generated by processing the echo collected by the ultrasound probe.
  • the medical image is explained by taking an ultrasound image as an example.
  • the medical image processing method includes the following steps:
  • the original ultrasound image includes an ultrasound body region 510.
  • the ultrasound body area corresponds to the target area scanned by the ultrasound probe.
  • the ultrasound image in the ultrasound body region 510 is a “pure” ultrasound body image required for scientific research.
  • the original ultrasound image also includes a sensitive area 520, in which sensitive information such as the time and location of the patient's examination are displayed.
  • S504 Perform grayscale processing on the original ultrasound image to obtain a grayscale image of the original ultrasound image.
  • S506 Use the conversion matrix to traverse the grayscale image of the original ultrasound image, and calculate the local entropy value of each pixel corresponding to the conversion matrix in the grayscale image.
  • S510 Normalize the local entropy matrix to obtain a local entropy grayscale image of the original ultrasound image (as shown in FIG. 5d).
  • S512 Perform image threshold segmentation on the local entropy gray image to obtain a binarized image of the local entropy gray image (as shown in FIG. 5e).
  • S514 Perform an open operation on the binarized image of the local entropy grayscale image to obtain the binarized image of the original ultrasound image (as shown in FIG. 5f).
  • the binarized image of the original ultrasound image includes multiple connected regions
  • the connected area with the largest area is determined as the connected area corresponding to the ultrasound body area and extracted (as shown in FIG. 5g).
  • the connected area is provided with corresponding corner coordinates.
  • S518 According to the corner coordinates of the connected area corresponding to the ultrasound body region, crop the original ultrasound image to obtain the ultrasound body image of the ultrasound body region (as shown in Fig. 5h and Fig. 5i).
  • the ultrasound body image of the ultrasound body region is the ultrasound image generated when the ultrasound probe scans the target part.
  • the ultrasound equipment includes an ultrasound display terminal and an ultrasound probe.
  • the ultrasonic display terminal applies an electrical signal to the ultrasonic probe through the ultrasonic cable so that the ultrasonic probe emits ultrasonic waves to the target site to be detected.
  • the ultrasonic probe receives the echo signal reflected by the target part and feeds the echo signal back to the ultrasonic display terminal through the ultrasonic cable.
  • the ultrasonic display terminal can generate the ultrasonic body image of the target part according to the echo signal.
  • the ultrasonic display terminal will respond according to the patient
  • the private information generates sensitive information. It is understandable that the sensitive information has nothing to do with the ultrasound emitted by the ultrasound probe.
  • the target part is a part of interest of the physician, such as liver, pancreas, uterus, etc.
  • the original ultrasound image is quickly segmented through the local entropy matrix, and the ultrasound image body area is completely segmented.
  • the obtained ultrasound body image not only contains complete ultrasound image information, and the ultrasound body image does not include any sensitive information.
  • a medical image processing device including: a first acquisition module 602, an image segmentation module 604, an area determination module 606, and an image extraction module 608, wherein:
  • the first acquisition module 602 is configured to acquire a local entropy grayscale image of an original medical image, the original medical image including a medical ontology region;
  • the image segmentation module 604 is configured to perform image segmentation on the local entropy grayscale image to obtain a binarized image of the original medical image; the binarized image of the original medical image includes a plurality of connected regions;
  • the area determining module 606 is configured to determine the connected area corresponding to the medical ontology area among the multiple connected areas according to the area of each of the connected areas;
  • the image extraction module 608 is configured to extract the image of the medical ontology region from the original medical image according to the connected regions corresponding to the medical ontology region.
  • the first acquisition module 602 includes a second acquisition module, a grayscale processing module, and an image traversal module; wherein:
  • the second acquisition module is used to acquire the original medical image
  • a grayscale processing module configured to perform grayscale processing on the original medical image to obtain a grayscale image of the original medical image
  • the image traversal module is used to traverse the grayscale image of the original medical image by using a conversion matrix of a preset size to obtain the local entropy grayscale image of the original medical image.
  • the image traversal module includes a local entropy calculation module, a gray value update module, and a normalization module; among them:
  • a local entropy calculation module configured to use the conversion matrix to traverse the grayscale image of the original medical image, and calculate the local entropy value of each pixel corresponding to the conversion matrix in the grayscale image;
  • a gray value update module configured to update the gray value of the pixel corresponding to the center position of the conversion matrix in the gray image to the calculated local entropy value to obtain the local entropy matrix
  • the normalization module is used to normalize the local entropy matrix to obtain the local entropy grayscale image of the original medical image.
  • the image segmentation module 604 is further configured to perform image threshold segmentation on the local entropy grayscale image to obtain a binarized image of the local entropy grayscale image;
  • the binarized image performs an open operation operation to obtain the binarized image of the original medical image.
  • the area determining module 606 is further configured to determine the connected area with the largest area among the multiple connected areas as the connected area corresponding to the medical ontology area.
  • the connected area is provided with corresponding corner coordinates; the image extraction module 608 is further configured to crop the original medical image according to the corner coordinates of the connected area corresponding to the medical ontology area, Obtain the medical image of the medical ontology area.
  • the original medical image is an ultrasound image including sensitive information
  • the medical image of the medical body region is an ultrasound image generated when an ultrasound probe scans a target part.
  • Each module in the above-mentioned medical image processing device can be implemented in whole or in part by software, hardware, and a combination thereof.
  • the foregoing modules may be embedded in the form of hardware or independent of the processor in the computer device, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the foregoing modules.
  • a computer device is provided.
  • the computer device may be a terminal, and its internal structure diagram may be as shown in FIG. 7.
  • the computer equipment includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus.
  • the processor of the computer device is used to provide calculation and control capabilities.
  • the memory of the computer device includes a non-volatile storage medium and an internal memory.
  • the non-volatile storage medium stores an operating system and a computer program.
  • the internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium.
  • the communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, an operator's network, NFC (near field communication) or other technologies.
  • the computer program is executed by the processor to realize a medical image processing method.
  • the display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, or it can be a button, trackball or touchpad set on the housing of the computer equipment , It can also be an external keyboard, touchpad, or mouse.
  • FIG. 7 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.
  • the specific computer device may Including more or fewer parts than shown in the figure, or combining some parts, or having a different arrangement of parts.
  • a computer device including a memory and a processor, and a computer program is stored in the memory.
  • the processor executes the computer program, the following steps are implemented: obtaining a partial entropy grayscale image of an original medical image,
  • the original medical image includes the medical ontology region; image segmentation is performed on the local entropy gray image to obtain a binarized image of the original medical image;
  • the binarized image of the original medical image includes a plurality of connected regions;
  • the area of the connected area determining the connected area corresponding to the medical ontology area among the multiple connected areas; extracting the medical ontology area from the original medical image according to the connected area corresponding to the medical ontology area Medical image.
  • the processor further implements the following steps when executing the computer program: acquiring an original medical image; performing gray-scale processing on the original medical image to obtain a gray-scale image of the original medical image; using a preset size conversion The matrix traverses the grayscale image of the original medical image to obtain the local entropy grayscale image of the original medical image.
  • the processor further implements the following steps when executing the computer program: traverse the grayscale image of the original medical image by using the conversion matrix, and calculate the value of each pixel corresponding to the conversion matrix in the grayscale image. Local entropy value; update the gray value of the pixel corresponding to the center position of the conversion matrix in the gray image to the calculated local entropy value to obtain the local entropy matrix; normalize the local entropy matrix, Obtain the local entropy grayscale image of the original medical image.
  • the processor further implements the following steps when executing the computer program: image threshold segmentation is performed on the local entropy gray image to obtain a binarized image of the local entropy gray image; The binarized image of the degree image is subjected to an open operation operation to obtain the binarized image of the original medical image.
  • the processor further implements the following step when executing the computer program: among the multiple connected areas, the connected area with the largest area is determined as the connected area corresponding to the medical ontology area.
  • the connected area is provided with corresponding corner coordinates; the processor further implements the following steps when executing the computer program: according to the corner coordinates of the connected area corresponding to the medical ontology area, compare the original medical image Cutting is performed to obtain the medical image of the medical body region.
  • the original medical image is an ultrasound image including sensitive information
  • the medical image of the medical body region is an ultrasound image generated when an ultrasound probe scans a target part.
  • a computer-readable storage medium on which a computer program is stored.
  • the following steps are implemented: Obtain a local entropy grayscale image of an original medical image, the original medical image Including the medical ontology region; performing image segmentation on the local entropy grayscale image to obtain a binarized image of the original medical image; the binarized image of the original medical image includes a plurality of connected regions; The area of the region, the connected region corresponding to the medical ontology region is determined among the multiple connected regions; the medical image of the medical ontology region is extracted from the original medical image according to the connected region corresponding to the medical ontology region .
  • the following steps are also implemented: obtaining an original medical image; performing gray-scale processing on the original medical image to obtain a gray-scale image of the original medical image; using a preset size
  • the conversion matrix traverses the grayscale image of the original medical image to obtain the local entropy grayscale image of the original medical image.
  • the following steps are also implemented: use the conversion matrix to traverse the grayscale image of the original medical image, and calculate each pixel corresponding to the conversion matrix in the grayscale image Update the gray value of the pixel corresponding to the center position of the conversion matrix in the gray image to the calculated local entropy value to obtain the local entropy matrix; normalize the local entropy matrix , To obtain the local entropy grayscale image of the original medical image.
  • image threshold segmentation is performed on the local entropy gray image to obtain a binarized image of the local entropy gray image;
  • the binarized image of the gray-scale image performs an open operation to obtain the binarized image of the original medical image.
  • the following step is further implemented: among the multiple connected areas, the connected area with the largest area is determined as the connected area corresponding to the medical ontology area.
  • the connected area is provided with corresponding corner coordinates; when the computer program is executed by the processor, the following steps are further implemented: according to the corner coordinates of the connected area corresponding to the medical ontology area, compare the original medical The image is cropped to obtain the medical image of the medical ontology area.
  • the original medical image is an ultrasound image including sensitive information
  • the medical image of the medical body region is an ultrasound image generated when an ultrasound probe scans a target part.
  • Non-volatile memory may include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, or optical storage.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM may be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Quality & Reliability (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

一种医学图像的处理方法、装置、计算机设备和存储介质，通过获取原始医学图像的局部熵灰度图像，所述原始医学图像包括医学本体区域（S102）；对所述局部熵灰度图像进行图像分割，得到所述原始医学图像的二值化图像（S104），并确定二值化图像中的多个连通区域；从而根据各所述连通区域的面积确定所述医学本体区域对应的连通区域（S106）；根据所述医学本体区域对应的连通区域，可以准确且完整地从所述原始医学图像中提取所述医学本体区域的医学图像（S108），对医学图像中的敏感信息进行脱敏。

Description

医学图像的处理方法、装置、计算机设备和存储介质 技术领域
本申请涉及医疗设备技术领域，特别是涉及一种医学图像的处理方法、装置、计算机设备和存储介质。
背景技术
随着医学影像技术的发展，医学影像技术涵盖有计算机断层扫描(CT)、核磁共振(MRI)、超声成像(US)、数字剪影血管造影术(DSA)等。为了规范图像机器相关信息的交换，消除各医学数字图像在格式、传输方式上的差异，DICOM(Digital imaging and Communications in Medicine，医学影像成像和通讯标准)标准诞生。DICOM医学图像的特点是图像分辨率高，图像质量高。但同时其数据量也是巨大，在对其存档、传输及管理时会存在一些难题。因此，在很多情况下将DICOM医学图像以压缩且带有杂余信息的图像形式保存下来的，且保存的医学图像中包含有医学图像和不属于医学图像的敏感信息(比如病人编号、检查时间、检查地点等个人隐私信息)。
目前，医疗科研机构或医疗器械公司从医院获取医学图像多为此类，对于医疗科研机构或医疗器械公司来说，医学图像中的敏感信息对后续的图像处理或者科学研究产生很大的负面影响，因此，需要从医学图像数据中提取医学图像以对医学图像中的敏感信息进行脱敏。
发明内容
基于此，有必要针对上述技术问题，提供一种能够对医学图像进行脱敏的医学图像的处理方法、装置、计算机设备和存储介质。
一种医学图像的处理方法，所述方法包括：
获取原始医学图像的局部熵灰度图像，所述原始医学图像包括医学本体区域；
对所述局部熵灰度图像进行图像分割，得到所述原始医学图像的二值化图 像；所述原始医学图像的二值化图像包括多个连通区域；
根据各所述连通区域的面积，在所述多个连通区域中确定所述医学本体区域对应的连通区域；
根据所述医学本体区域对应的连通区域，从所述原始医学图像中提取所述医学本体区域的医学图像。
在其中一个实施例中，所述获取原始医学图像的局部熵灰度图像，包括：
获取原始医学图像；
对所述原始医学图像进行灰度处理，得到所述原始医学图像的灰度图像；
利用预设大小的转换矩阵遍历所述原始医学图像的灰度图像，得到所述原始医学图像的局部熵灰度图像。
在其中一个实施例中，所述利用预设大小的转换矩阵遍历所述原始医学图像的灰度图像，得到所述原始医学图像的局部熵灰度图像，包括：
利用所述转换矩阵遍历所述原始医学图像的灰度图像，计算所述灰度图像中所述转换矩阵对应的各像素点的局部熵值；
将所述灰度图像中所述转换矩阵中心位置对应的像素点的灰度值更新为计算得到的局部熵值，得到局部熵矩阵；
对所述局部熵矩阵进行归一化，得到所述原始医学图像的局部熵灰度图像。
在其中一个实施例中，所述对所述局部熵灰度图像进行图像分割，得到所述原始医学图像的二值化图像，包括：
对所述局部熵灰度图像进行图像阈值分割，得到所述局部熵灰度图像的二值化图像；
对所述局部熵灰度图像的二值化图像进行开运算操作，得到所述原始医学图像的二值化图像。
在其中一个实施例中，所述根据各所述连通区域的面积，在所述多个连通区域中确定所述医学本体区域对应的连通区域，包括：
在所述多个连通区域中，将面积最大的连通区域确定为所述医学本体区域对应的连通区域。
在其中一个实施例中，所述连通区域设有对应的角点坐标；所述根据所述 医学本体区域对应的连通区域，从所述原始医学图像中提取所述医学本体区域的医学图像，包括：
根据所述医学本体区域对应的连通区域的角点坐标，对所述原始医学图像进行裁剪，得到所述医学本体区域的医学图像。
在其中一个实施例中，所述原始医学图像为包括敏感信息的超声图像；所述医学本体区域的医学图像为超声探头扫查目标部位时生成的超声图像。
一种医学图像的处理装置，所述装置包括：
第一获取模块，用于获取原始医学图像的局部熵灰度图像，所述原始医学图像包括医学本体区域；
图像分割模块，用于对所述局部熵灰度图像进行图像分割，得到所述原始医学图像的二值化图像；所述原始医学图像的二值化图像包括多个连通区域；
区域确定模块，用于根据各所述连通区域的面积，在所述多个连通区域中确定所述医学本体区域对应的连通区域；
图像提取模块，用于根据所述医学本体区域对应的连通区域，从所述原始医学图像中提取所述医学本体区域的图像。
一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现上述任一项所述的医学图像的处理方法的步骤。
一种计算机可读存储介质，其上存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现上述任一项所述的医学图像的处理方法的步骤。
上述医学图像的处理方法、装置、计算机设备和存储介质，通过获取原始医学图像的局部熵灰度图像，所述原始医学图像包括医学本体区域；对所述局部熵灰度图像进行图像分割，得到所述原始医学图像的二值化图像，并确定二值化图像中的多个连通区域；从而根据各所述连通区域的面积确定所述医学本体区域对应的连通区域；进一步地，由于二值化图像与原始医学图像对应，则根据所述医学本体区域对应的连通区域，可以准确且完整地从所述原始医学图像中提取所述医学本体区域的医学图像，对医学图像中的敏感信息进行脱敏。
附图说明
图1为一个实施例中医学图像的处理方法的流程示意图；
图2a为一个实施例中步骤S102的流程示意图；
图2b为一个实施例中局部熵提取的流程示意图；
图4为另一个实施例中局部熵提取的流程示意图；
图3为一个实施例中步骤S206的流程示意图；
图4a为一个实施例中步骤S104的流程示意图；
图4b为一个实施例中腐蚀操作的流程示意图；
图4c为一个实施例中膨胀操作的流程示意图；
图5a为一个实施例中超声图像的处理方法的流程示意图；
图5b至5c为一个实施例中原始超声图像的示意图；
图5d为一个实施例中局部熵灰度图像的示意图；
图5e为一个实施例中二值化图像的示意图；
图5f为一个实施例中开运算操作后的二值化图像的示意图；
图5g为一个实施例中超声本体区域(面积最大连通区域)的示意图；
图5h至5i为一个实施例中提取的超声本体图像的示意图；
图6为一个实施例中医学图像的处理装置的结构框图；
图7为一个实施例中计算机设备的内部结构图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
在一个实施例中，如图1所示，提供了一种医学图像的处理方法，该方法包括以下步骤：
S102，获取原始医学图像的局部熵灰度图像，原始医学图像包括医学本体区域。
其中，原始医学图像是通过计算机断层扫描(CT)、核磁共振(MRI)、超 声成像(US)、数字剪影血管造影术(DSA)等医学影像技术获得的医学影像图像，其包含有医学本体图像和不属于医学本体图像的敏感信息。医学本体图像是对感兴趣部位进行扫描得到的图像。且医学本体区域为原始医学图像中与患者感兴趣部位对应的部分区域。在得到感兴趣部位对应的医学本体图像后，成像设备根据病人编号、检查时间、检查地点等个人隐私信息生成敏感信息并将其显示出来，生成原始医学图像。感兴趣部位可以是如子宫、心脏等身体器官。原始医学图像可以是将DICOM(Digital imaging and Communications in Medicine，医学影像成像和通讯标准)医学图像压缩且带有杂余信息的图像形式保存下来的图像。原始医学图像的格式可以是jpg格式。
具体地，从计算机本地或者与计算机通信连接的计算机设备获取原始医学图像的局部熵灰度图像。局部熵灰度图像是指对原始医学图像对应的灰度图像进行局部熵计算后，并对原始医学图像中各像素灰度值更新后得到的灰度图像。
局部熵是指在数字图像有限的N*N(如5*5)范围内计算得到的熵。而数字图像的熵是一种特殊的统计形式，它反映了图像中灰度分布的聚集特征所包含的信息量，一张灰度数字图像的熵表达式为：
Figure PCTCN2020133036-appb-000001
其中，i为像素的灰度值，p i为该像素值i在整幅数字图像中出现的概率。
S104，对局部熵灰度图像进行图像分割，得到原始医学图像的二值化图像。
其中，图像分割是指把图像分成若干个特定的、具有独特性质的区域并提取感兴趣区域或者目标部位的技术和过程。比如，本实施例中，从原始医学图像提取医学本体图像。二值化图像中各像素点的灰度值为0或255，整个图像呈现出明显的黑白效果。连通区域(Connected Component)一般是指图像中具有相同像素值且位置相邻的像素点组成的图像区域。且原始医学图像的二值化图像包括多个连通区域。各个连通区域的面积可能不等，也可能相等。具体地，为了从原始医学图像中提取医学本体图像，根据局部熵灰度图像中各像素点的灰度值设置分割阈值，将局部熵灰度图像中各像素点的灰度值更新，比如更新为0或者255，实现对原始医学图像的局部熵灰度图像进行图像分割，得到原始 医学图像的二值化图像。从而根据二值化图像中各像素点的灰度值，确定该二值化图像中的连通区域。
S106，根据各连通区域的面积，在多个连通区域中确定医学本体区域对应的连通区域。
具体地，原始医学图像包含有医学本体图像对应的医学本体区域和敏感信息对应的敏感区域。医学本体区域与敏感区域分别对应有各自的连通区域。由于医学本体区域与敏感区域在原始医学图像具有不同大小的面积，则可以根据连通区域的面积，在二值化图像的多个连通区域中确定与医学本体区域对应的连通区域。
S108，根据医学本体区域对应的连通区域，从原始医学图像中提取医学本体区域的医学图像。
具体地，根据连通区域的面积，在二值化图像的多个连通区域中确定与医学本体区域对应的连通区域，从而确定了医学本体区域在二值化图像中的位置或者分布情况。又因为二值化图像与原始医学图像对应，即可得知医学本体区域在原始医学图像中的位置或者分布情况，从而可以从原始医学图像中提取医学本体区域的医学图像。
上述医学图像的处理方法中，通过获取原始医学图像的局部熵灰度图像，原始医学图像包括医学本体区域；对局部熵灰度图像进行图像分割，得到原始医学图像的二值化图像，并确定二值化图像中的多个连通区域；从而根据各连通区域的面积确定医学本体区域对应的连通区域；进一步地，由于二值化图像与原始医学图像对应，则根据医学本体区域对应的连通区域，可以准确且完整地从原始医学图像中提取医学本体区域的医学图像，对医学图像中的敏感信息进行脱敏。进一步地，提取得到的医学图像包含有完整的医学图像信息，解决了传统技术中医学图像信息缺失的技术问题。
在一个实施例中，如图2a所示，在步骤S102中，获取原始医学图像的局部熵灰度图像，包括以下步骤：
S202，获取原始医学图像；
S204，对原始医学图像进行灰度处理，得到原始医学图像的灰度图像；
S206，利用预设大小的转换矩阵遍历原始医学图像的灰度图像，得到原始医学图像的局部熵灰度图像。
其中，转换矩阵是预设大小的N*N的矩阵，用于在原始医学图像的灰度图像中圈定N*N的范围，并计算出N*N范围内的局部熵值。具体地，从计算机本地或者与计算机通信连接的计算机设备获取原始医学图像。原始医学图像可以是具有色彩的，为了方便后续图像处理的过程，对原始医学图像进行灰度处理，得到原始医学图像的灰度图像。如图2b所示，在原始医学图像的灰度图像中，根据5*5的转换矩阵圈定范围内各像素点的像素值对原始医学图像进行局部熵的提取，从而利用5*5的转换矩阵遍历原始医学图像的灰度图像，得到原始医学图像的局部熵灰度图像。
本实施例中，通过对原始医学图像进行灰度处理，得到原始医学图像的灰度图像；并利用预设大小的转换矩阵遍历原始医学图像的灰度图像，得到原始医学图像的局部熵灰度图像，能够将医学本体区域与敏感区域在原始医学图像中突出显示，为后续医学本体图像的提取提供了基础。
在一个实施例中，如图3所示，在步骤S206中，利用预设大小的转换矩阵遍历原始医学图像的灰度图像，得到原始医学图像的局部熵灰度图像，包括：
S302，利用转换矩阵遍历原始医学图像的灰度图像，计算灰度图像中转换矩阵对应的各像素点的局部熵值。
S304，将灰度图像中转换矩阵中心位置对应的像素点的灰度值更新为计算得到的局部熵值，得到局部熵矩阵。
S306，对局部熵矩阵进行归一化，得到原始医学图像的局部熵灰度图像。
具体地，继续如图2b所示，在原始医学图像的灰度图像中，根据N*N的转换矩阵圈定范围内各像素点的像素值进行计算，得到该转换矩阵在N*N范围内的局部熵值。利用该N*N范围内的局部熵值更新N*N矩阵中心位置处的像素点的灰度值，依次类推，利用N*N的转换矩阵遍历原始医学图像的灰度图像，从而得到局部熵矩阵。进一步地，将区域熵矩阵归一化到预设灰度值比如(0值255)之间，并将其设定为每个像素八位深，即得到一副单通道、八位深度的区域熵灰度图像。可以理解的是，N的大小属于经验值，可依据实际情况而取值。
本实施例中，利用转换矩阵遍历原始医学图像的灰度图像，计算灰度图像中转换矩阵对应的各像素点的局部熵值；将灰度图像中转换矩阵中心位置对应的像素点的灰度值更新为计算得到的局部熵值，得到局部熵矩阵；对局部熵矩阵进行归一化，得到原始医学图像的局部熵灰度图像。能够进一步地将医学本体区域与敏感区域在原始医学图像中突出显示，确保医学本体图像提取的准确性和完整性。
在一个实施例中，如图4a所示，在步骤S104中，对局部熵灰度图像进行图像分割，得到原始医学图像的二值化图像，包括：
S402，对局部熵灰度图像进行图像阈值分割，得到局部熵灰度图像的二值化图像；
S404，对局部熵灰度图像的二值化图像进行开运算操作，得到原始医学图像的二值化图像。
其中，图像阈值分割可以采用大津法(OTSU法)。大津法为本领艺术人员所公知，在此不再赘述。开运算操作包括对图像先腐蚀再膨胀的过程，其是针对二值化图像进行的操作，以改变图像的大小、粗细、粘连等形态，从而优化二值化图像的轮廓。如图4b所示，腐蚀操作是将物体的边缘加以腐蚀。具体的操作方法为M*N的矩形作为模板对图像中的每一个像素做如下处理：像素x至于模板的中心，根据模板的大小遍历所有被模板覆盖的其他像素，修改像素x的值为所有像素中最小的值。这样操作的结果是会将图像***的突出点加以腐蚀。如图4c所示，与腐蚀操作相反，膨胀操作是将图像的轮廓加以膨胀。操作方法与腐蚀操作类似，M*N的矩形作为模板`对图像的每个像素做遍历处理。不同之处在于修改像素的值不是所有像素中最小的值，而是最大的值。这样操作的结果会将图像***的突出点连接并向外延伸。
具体地，为了从原始医学图像中提取医学本体图像，根据局部熵灰度图像中各像素点的灰度值设置分割阈值，将局部熵灰度图像中各像素点的灰度值更新，比如更新为0或者255，实现对原始医学图像的局部熵灰度图像进行图像阈值分割，得到原始医学图像的二值化图像。进一步地，为了优化二值化图像中的轮廓，对局部熵灰度图像的二值化图像进行先腐蚀再膨胀的操作，得到原始 医学图像的二值化图像，从而根据二值化图像中各像素点的灰度值，确定该二值化图像中的连通区域。
本实施例中，通过对局部熵灰度图像进行图像阈值分割，得到局部熵灰度图像的二值化图像，实现了医学本体区域与敏感区域在原始医学图像中的突出显示，并进一步地对局部熵灰度图像的二值化图像进行开运算操作，得到原始医学图像的二值化图像，优化二值化图像中的轮廓，确保连通区域的准确性，从而可以准确且完整地从原始医学图像中提取医学本体区域的医学图像，对医学图像中的敏感信息进行脱敏。
在一个实施例中，根据各连通区域的面积，在多个连通区域中确定医学本体区域对应的连通区域，包括：在多个连通区域中，将面积最大的连通区域确定为医学本体区域对应的连通区域。
具体地，结合实际情况，医学本体区域与敏感区域相比，医学本体区域是原始医学图像中面积最大的区域。因此，在多个连通区域中，将面积最大的连通区域确定为医学本体区域对应的连通区域。
在一个实施例中，连通区域设有对应的角点坐标。根据医学本体区域对应的连通区域，从原始医学图像中提取医学本体区域的医学图像，包括：根据医学本体区域对应的连通区域的角点坐标，对原始医学图像进行裁剪，得到医学本体区域的医学图像。
其中，角点是连通区域所具有的多边形形状的顶点。连通区域在原始医学图像的二值化图像中具有角点坐标，且二值化图像与原始医学图像对应，可知医学本体区域在原始医学图像的位置以四边形的连通区域为例进行说明，在连通区域内各像素点对应有坐标值，从中分别获取最大x轴坐标值x1以及最小的x轴坐标值x2，并获取最大y轴坐标值y1以及最小的y轴坐标值y2。则角点坐标分别为(x1,y1)、(x1,y2)、(x2,y1)、(x2,y2)。具体地，根据医学本体区域对应的连通区域的角点坐标，确定医学本体区域在原始医学图像的位置，从而根据医学本体区域在原始医学图像的位置，对原始医学图像进行裁剪，从而得到医学本体区域的医学图像。
示例性地，连通区域的形状为矩形，角点坐标即为矩形的四个顶点的坐标， 矩形框内的区域即为医学本体区域，则根据该矩形的四个顶点从原始医学图像提取医学本体区域的医学图像。
在一个实施例中，原始医学图像为包括敏感信息的超声图像；医学本体区域的医学图像为超声探头扫查目标部位时生成的超声图像。其中，该超声图像是对超声探头收集的回波进行处理而生成的。
在一个实施例中，医学图像以超声图像为例进行说明，如图5a所示，医学图像的处理方法包括以下步骤：
S502，获取原始超声图像。
其中，如图5b和5c所示，原始超声图像包括超声本体区域510。超声本体区域与超声探头扫查目标部位对应。超声本体区域510内的超声图像是科研工作需要的“纯粹”的超声本体图像。进一步地，原始超声图像还包括敏感区域520，敏感区域内显示患者检查时间、地点等敏感信息。
S504，对原始超声图像进行灰度处理，得到原始超声图像的灰度图像。
S506，利用转换矩阵遍历原始超声图像的灰度图像，计算灰度图像中转换矩阵对应的各像素点的局部熵值。
S508，将灰度图像中转换矩阵中心位置对应的像素点的灰度值更新为计算得到的局部熵值，得到局部熵矩阵。
S510，对局部熵矩阵进行归一化，得到原始超声图像的局部熵灰度图像(如图5d所示)。
S512，对局部熵灰度图像进行图像阈值分割，得到局部熵灰度图像的二值化图像(如图5e所示)。
S514，对局部熵灰度图像的二值化图像进行开运算操作，得到原始超声图像的二值化图像(如图5f所示)。
其中，原始超声图像的二值化图像包括多个连通区域；
S516，在多个连通区域中，将面积最大的连通区域确定为超声本体区域对应的连通区域并提取(如图5g所示)。
其中，连通区域设有对应的角点坐标。
S518，根据超声本体区域对应的连通区域的角点坐标，对原始超声图像进 行裁剪，得到超声本体区域的超声本体图像(如图5h和图5i所示)。
其中，超声本体区域的超声本体图像为超声探头扫查目标部位时生成的超声图像。超声设备包括超声显示终端以及超声探头。在进行医学超声检测时，医师操作超声探头在待检测的目标部位或者感兴趣部位进行扫查。超声显示终端通过超声线缆在超声探头上施加电信号使得超声探头向待检测的目标部位发出超声波。超声探头接收目标部位反射的回波信号并将该回波信号通过超声线缆反馈给超声显示终端，超声显示终端根据回波信号可以生成目标部位的超声本体图像，接着，超声显示终端会根据患者的隐私信息生成敏感信息，可以理解的是，敏感信息与超声探头发射的超声波没有关系。目标部位为医师的感兴趣部位，例如肝脏、胰腺、子宫等。
本实施例中，结合原始超声图像的特点，通过局部熵矩阵快速对原始超声图像进行图像分割，完整地将超声图像本体区域分割出来。利用超声本体区域的角点坐标提取超声本体图像，得到的超声本体图像不仅包含有完整的超声图像信息，且该超声本体图像不包括任何涉敏信息。
应该理解的是，虽然上述流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，这些步骤可以以其它的顺序执行。而且，上述流程图中的至少一部分步骤可以包括多个步骤或者多个阶段，这些步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，这些步骤或者阶段的执行顺序也不必然是依次进行，而是可以与其它步骤或者其它步骤中的步骤或者阶段的至少一部分轮流或者交替地执行。
在一个实施例中，如图6所示，提供了一种医学图像的处理装置，包括：第一获取模块602、图像分割模块604、区域确定模块606和图像提取模块608，其中：
第一获取模块602，用于获取原始医学图像的局部熵灰度图像，所述原始医学图像包括医学本体区域；
图像分割模块604，用于对所述局部熵灰度图像进行图像分割，得到所述原始医学图像的二值化图像；所述原始医学图像的二值化图像包括多个连通区域；
区域确定模块606，用于根据各所述连通区域的面积，在所述多个连通区域中确定所述医学本体区域对应的连通区域；
图像提取模块608，用于根据所述医学本体区域对应的连通区域，从所述原始医学图像中提取所述医学本体区域的图像。
在一个实施例中，第一获取模块602包括第二获取模块、灰度处理模块和图像遍历模块；其中：
第二获取模块，用于获取原始医学图像；
灰度处理模块，用于对所述原始医学图像进行灰度处理，得到所述原始医学图像的灰度图像；
图像遍历模块，用于利用预设大小的转换矩阵遍历所述原始医学图像的灰度图像，得到所述原始医学图像的局部熵灰度图像。
在一个实施例中，图像遍历模块包括局部熵计算模块、灰度值更新模块和归一化模块；其中：
局部熵计算模块，用于利用所述转换矩阵遍历所述原始医学图像的灰度图像，计算所述灰度图像中所述转换矩阵对应的各像素点的局部熵值；
灰度值更新模块，用于将所述灰度图像中所述转换矩阵中心位置对应的像素点的灰度值更新为计算得到的局部熵值，得到局部熵矩阵；
归一化模块，用于对所述局部熵矩阵进行归一化，得到所述原始医学图像的局部熵灰度图像。
在一个实施例中，图像分割模块604，还用于对所述局部熵灰度图像进行图像阈值分割，得到所述局部熵灰度图像的二值化图像；对所述局部熵灰度图像的二值化图像进行开运算操作，得到所述原始医学图像的二值化图像。
在一个实施例中，区域确定模块606，还用于在所述多个连通区域中，将面积最大的连通区域确定为所述医学本体区域对应的连通区域。
在一个实施例中，所述连通区域设有对应的角点坐标；图像提取模块608，还用于根据所述医学本体区域对应的连通区域的角点坐标，对所述原始医学图像进行裁剪，得到所述医学本体区域的医学图像。
在一个实施例中，所述原始医学图像为包括敏感信息的超声图像；所述医 学本体区域的医学图像为超声探头扫查目标部位时生成的超声图像。
关于医学图像的处理装置的具体限定可以参见上文中对于医学图像的处理方法的限定，在此不再赘述。上述医学图像的处理装置中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于计算机设备中的处理器中，也可以以软件形式存储于计算机设备中的存储器中，以便于处理器调用执行以上各个模块对应的操作。
在一个实施例中，提供了一种计算机设备，该计算机设备可以是终端，其内部结构图可以如图7所示。该计算机设备包括通过***总线连接的处理器、存储器、通信接口、显示屏和输入装置。其中，该计算机设备的处理器用于提供计算和控制能力。该计算机设备的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作***和计算机程序。该内存储器为非易失性存储介质中的操作***和计算机程序的运行提供环境。该计算机设备的通信接口用于与外部的终端进行有线或无线方式的通信，无线方式可通过WIFI、运营商网络、NFC(近场通信)或其他技术实现。该计算机程序被处理器执行时以实现一种医学图像的处理方法。该计算机设备的显示屏可以是液晶显示屏或者电子墨水显示屏，该计算机设备的输入装置可以是显示屏上覆盖的触摸层，也可以是计算机设备外壳上设置的按键、轨迹球或触控板，还可以是外接的键盘、触控板或鼠标等。
本领域技术人员可以理解，图7中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
在一个实施例中，提供了一种计算机设备，包括存储器和处理器，存储器中存储有计算机程序，该处理器执行计算机程序时实现以下步骤：获取原始医学图像的局部熵灰度图像，所述原始医学图像包括医学本体区域；对所述局部熵灰度图像进行图像分割，得到所述原始医学图像的二值化图像；所述原始医学图像的二值化图像包括多个连通区域；根据各所述连通区域的面积，在所述多个连通区域中确定所述医学本体区域对应的连通区域；根据所述医学本体区 域对应的连通区域，从所述原始医学图像中提取所述医学本体区域的医学图像。
在一个实施例中，处理器执行计算机程序时还实现以下步骤：获取原始医学图像；对所述原始医学图像进行灰度处理，得到所述原始医学图像的灰度图像；利用预设大小的转换矩阵遍历所述原始医学图像的灰度图像，得到所述原始医学图像的局部熵灰度图像。
在一个实施例中，处理器执行计算机程序时还实现以下步骤：利用所述转换矩阵遍历所述原始医学图像的灰度图像，计算所述灰度图像中所述转换矩阵对应的各像素点的局部熵值；将所述灰度图像中所述转换矩阵中心位置对应的像素点的灰度值更新为计算得到的局部熵值，得到局部熵矩阵；对所述局部熵矩阵进行归一化，得到所述原始医学图像的局部熵灰度图像。
在一个实施例中，处理器执行计算机程序时还实现以下步骤：对所述局部熵灰度图像进行图像阈值分割，得到所述局部熵灰度图像的二值化图像；对所述局部熵灰度图像的二值化图像进行开运算操作，得到所述原始医学图像的二值化图像。
在一个实施例中，处理器执行计算机程序时还实现以下步骤：在所述多个连通区域中，将面积最大的连通区域确定为所述医学本体区域对应的连通区域。
在一个实施例中，所述连通区域设有对应的角点坐标；处理器执行计算机程序时还实现以下步骤：根据所述医学本体区域对应的连通区域的角点坐标，对所述原始医学图像进行裁剪，得到所述医学本体区域的医学图像。
在一个实施例中，所述原始医学图像为包括敏感信息的超声图像；所述医学本体区域的医学图像为超声探头扫查目标部位时生成的超声图像。
在一个实施例中，提供了一种计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现以下步骤：获取原始医学图像的局部熵灰度图像，所述原始医学图像包括医学本体区域；对所述局部熵灰度图像进行图像分割，得到所述原始医学图像的二值化图像；所述原始医学图像的二值化图像包括多个连通区域；根据各所述连通区域的面积，在所述多个连通区域中确定所述医学本体区域对应的连通区域；根据所述医学本体区域对应的连通区域，从所述原始医学图像中提取所述医学本体区域的医学图像。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：获取原始医学图像；对所述原始医学图像进行灰度处理，得到所述原始医学图像的灰度图像；利用预设大小的转换矩阵遍历所述原始医学图像的灰度图像，得到所述原始医学图像的局部熵灰度图像。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：利用所述转换矩阵遍历所述原始医学图像的灰度图像，计算所述灰度图像中所述转换矩阵对应的各像素点的局部熵值；将所述灰度图像中所述转换矩阵中心位置对应的像素点的灰度值更新为计算得到的局部熵值，得到局部熵矩阵；对所述局部熵矩阵进行归一化，得到所述原始医学图像的局部熵灰度图像。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：对所述局部熵灰度图像进行图像阈值分割，得到所述局部熵灰度图像的二值化图像；对所述局部熵灰度图像的二值化图像进行开运算操作，得到所述原始医学图像的二值化图像。
在一个实施例中，计算机程序被处理器执行时还实现以下步骤：在所述多个连通区域中，将面积最大的连通区域确定为所述医学本体区域对应的连通区域。
在一个实施例中，所述连通区域设有对应的角点坐标；计算机程序被处理器执行时还实现以下步骤：根据所述医学本体区域对应的连通区域的角点坐标，对所述原始医学图像进行裁剪，得到所述医学本体区域的医学图像。
在一个实施例中，所述原始医学图像为包括敏感信息的超声图像；所述医学本体区域的医学图像为超声探头扫查目标部位时生成的超声图像。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一非易失性计算机可读取存储介质中，该计算机程序在执行时，可包括如上述各方法的实施例的流程。其中，本申请所提供的各实施例中所使用的对存储器、存储、数据库或其它介质的任何引用，均可包括非易失性和易失性存储器中的至少一种。非易失性存储器可包括只读存储器(Read-Only Memory，ROM)、磁带、软盘、闪存或光存储器等。易失性存储器可包括随机存取存储器(Random  Access Memory，RAM)或外部高速缓冲存储器。作为说明而非局限，RAM可以是多种形式，比如静态随机存取存储器(Static Random Access Memory，SRAM)或动态随机存取存储器(Dynamic Random Access Memory，DRAM)等。
以上实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上所述实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请专利的保护范围应以所附权利要求为准。

Claims (10)

  1. 一种医学图像的处理方法，其特征在于，所述方法包括：
    获取原始医学图像的局部熵灰度图像，所述原始医学图像包括医学本体区域；
    对所述局部熵灰度图像进行图像分割，得到所述原始医学图像的二值化图像；所述原始医学图像的二值化图像包括多个连通区域；
    根据各所述连通区域的面积，在所述多个连通区域中确定所述医学本体区域对应的连通区域；
    根据所述医学本体区域对应的连通区域，从所述原始医学图像中提取所述医学本体区域的医学图像。
  2. 根据权利要求1所述的方法，其特征在于，所述获取原始医学图像的局部熵灰度图像，包括：
    获取原始医学图像；
    对所述原始医学图像进行灰度处理，得到所述原始医学图像的灰度图像；
    利用预设大小的转换矩阵遍历所述原始医学图像的灰度图像，得到所述原始医学图像的局部熵灰度图像。
  3. 根据权利要求2所述的方法，其特征在于，所述利用预设大小的转换矩阵遍历所述原始医学图像的灰度图像，得到所述原始医学图像的局部熵灰度图像，包括：
    利用所述转换矩阵遍历所述原始医学图像的灰度图像，计算所述灰度图像中所述转换矩阵对应的各像素点的局部熵值；
    将所述灰度图像中所述转换矩阵中心位置对应的像素点的灰度值更新为计算得到的局部熵值，得到局部熵矩阵；
    对所述局部熵矩阵进行归一化，得到所述原始医学图像的局部熵灰度图像。
  4. 根据权利要求1所述的方法，其特征在于，所述对所述局部熵灰度图像进行图像分割，得到所述原始医学图像的二值化图像，包括：
    对所述局部熵灰度图像进行图像阈值分割，得到所述局部熵灰度图像的二值化图像；
    对所述局部熵灰度图像的二值化图像进行开运算操作，得到所述原始医学 图像的二值化图像。
  5. 根据权利要求1所述的方法，其特征在于，所述根据各所述连通区域的面积，在所述多个连通区域中确定所述医学本体区域对应的连通区域，包括：
    在所述多个连通区域中，将面积最大的连通区域确定为所述医学本体区域对应的连通区域。
  6. 根据权利要求1所述的方法，其特征在于，所述连通区域设有对应的角点坐标；所述根据所述医学本体区域对应的连通区域，从所述原始医学图像中提取所述医学本体区域的医学图像，包括：
    根据所述医学本体区域对应的连通区域的角点坐标，对所述原始医学图像进行裁剪，得到所述医学本体区域的医学图像。
  7. 根据权利要求1至6任一项所述的方法，其特征在于，所述原始医学图像为包括敏感信息的超声图像；所述医学本体区域的医学图像为超声探头扫查目标部位时生成的超声图像。
  8. 一种医学图像的处理装置，其特征在于，所述装置包括：
    第一获取模块，用于获取原始医学图像的局部熵灰度图像，所述原始医学图像包括医学本体区域；
    图像分割模块，用于对所述局部熵灰度图像进行图像分割，得到所述原始医学图像的二值化图像；所述原始医学图像的二值化图像包括多个连通区域；
    区域确定模块，用于根据各所述连通区域的面积，在所述多个连通区域中确定所述医学本体区域对应的连通区域；
    图像提取模块，用于根据所述医学本体区域对应的连通区域，从所述原始医学图像中提取所述医学本体区域的图像。
  9. 一种计算机设备，包括存储器和处理器，所述存储器存储有计算机程序，其特征在于，所述处理器执行所述计算机程序时实现权利要求1至7中任一项所述的方法的步骤。
  10. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现权利要求1至7中任一项所述的方法的步骤。
PCT/CN2020/133036 2020-06-18 2020-12-01 医学图像的处理方法、装置、计算机设备和存储介质 WO2021253732A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202010561269.2A CN113822839B (zh) 2020-06-18 2020-06-18 医学图像的处理方法、装置、计算机设备和存储介质
CN202010561269.2 2020-06-18

Publications (1)

Publication Number Publication Date
WO2021253732A1 true WO2021253732A1 (zh) 2021-12-23

Family

ID=78911885

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/133036 WO2021253732A1 (zh) 2020-06-18 2020-12-01 医学图像的处理方法、装置、计算机设备和存储介质

Country Status (2)

Country Link
CN (1) CN113822839B (zh)
WO (1) WO2021253732A1 (zh)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116630256A (zh) * 2023-05-15 2023-08-22 什维新智医疗科技(上海)有限公司 一种超声造影图像识别方法、***、电子设备及存储介质
CN116935493A (zh) * 2023-09-13 2023-10-24 成都市青羊大数据有限责任公司 一种教育管理***

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024119323A1 (zh) * 2022-12-05 2024-06-13 深圳华大生命科学研究院 样本图像的组织分割方法、***、设备及介质

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106295588A (zh) * 2016-08-17 2017-01-04 电子科技大学 一种白带显微图像中白细胞的自动识别方法
CN107845098A (zh) * 2017-11-14 2018-03-27 南京理工大学 基于随机森林和模糊聚类的肝癌图像全自动分割方法
CN107909590A (zh) * 2017-11-15 2018-04-13 北京工业大学 一种基于Snake改进算法的IVUS图像外膜边缘分割方法
CN108765409A (zh) * 2018-06-01 2018-11-06 电子科技大学 一种基于ct图像的候选结节的筛选方法
WO2019041590A1 (zh) * 2017-08-31 2019-03-07 中国科学院微电子研究所 任意角度的边缘检测方法
CN110378924A (zh) * 2019-07-19 2019-10-25 合肥学院 基于局部熵的水平集图像分割方法

Family Cites Families (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103914856A (zh) * 2014-04-14 2014-07-09 贵州电网公司输电运行检修分公司 一种基于熵值的运动目标检测方法
CN104376260B (zh) * 2014-11-20 2017-06-30 东华大学 一种基于香农信息熵的恶意代码可视化分析方法
CN105427320B (zh) * 2015-11-30 2018-07-06 威海北洋电气集团股份有限公司 一种图像分割提取方法
CN106373138B (zh) * 2016-08-30 2019-07-02 海纳医信（北京）软件科技有限责任公司 用于提取牙弓曲线的图像处理方法和装置
CN106815853B (zh) * 2016-12-14 2020-06-05 海纳医信（北京）软件科技有限责任公司 对眼底图像中视网膜血管的分割方法和装置
CN107229910A (zh) * 2017-05-18 2017-10-03 北京环境特性研究所 一种遥感图像结冰湖泊检测方法及其***
CN107239666B (zh) * 2017-06-09 2020-10-09 孟群 一种对医疗影像数据进行脱敏处理的方法及***
CN108629773B (zh) * 2018-05-10 2021-06-18 北京红云智胜科技有限公司 建立训练识别心脏血管类型的卷积神经网络数据集的方法
CN108765335B (zh) * 2018-05-25 2022-08-02 电子科技大学 一种基于遥感图像的森林火灾检测方法
CN108765406A (zh) * 2018-05-31 2018-11-06 电子科技大学 一种基于红外遥感图像的雪山检测方法
CN109063740A (zh) * 2018-07-05 2018-12-21 高镜尧 超声影像关键目标的检测模型构建及检测方法、装置
CN110288594B (zh) * 2019-07-02 2021-06-04 河北农业大学 一种植物冠层结构性状分析方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106295588A (zh) * 2016-08-17 2017-01-04 电子科技大学 一种白带显微图像中白细胞的自动识别方法
WO2019041590A1 (zh) * 2017-08-31 2019-03-07 中国科学院微电子研究所 任意角度的边缘检测方法
CN107845098A (zh) * 2017-11-14 2018-03-27 南京理工大学 基于随机森林和模糊聚类的肝癌图像全自动分割方法
CN107909590A (zh) * 2017-11-15 2018-04-13 北京工业大学 一种基于Snake改进算法的IVUS图像外膜边缘分割方法
CN108765409A (zh) * 2018-06-01 2018-11-06 电子科技大学 一种基于ct图像的候选结节的筛选方法
CN110378924A (zh) * 2019-07-19 2019-10-25 合肥学院 基于局部熵的水平集图像分割方法

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116630256A (zh) * 2023-05-15 2023-08-22 什维新智医疗科技(上海)有限公司 一种超声造影图像识别方法、***、电子设备及存储介质
CN116935493A (zh) * 2023-09-13 2023-10-24 成都市青羊大数据有限责任公司 一种教育管理***
CN116935493B (zh) * 2023-09-13 2024-01-02 成都市青羊大数据有限责任公司 一种教育管理***

Also Published As

Publication number Publication date
CN113822839A (zh) 2021-12-21
CN113822839B (zh) 2024-01-23

Similar Documents

Publication Publication Date Title
WO2021253732A1 (zh) 医学图像的处理方法、装置、计算机设备和存储介质
WO2020224406A1 (zh) 图像分类方法、计算机可读存储介质和计算机设备
CN111192356B (zh) 感兴趣区域的显示方法、装置、设备和存储介质
US11633146B2 (en) Automated co-registration of prostate MRI data
CN111932520A (zh) 医学图像显示方法、查看设备以及计算机设备
US8401285B1 (en) Methods, apparatuses, and computer program products for controlling luminance of non-tissue objects within an image
US9058650B2 (en) Methods, apparatuses, and computer program products for identifying a region of interest within a mammogram image
US11276490B2 (en) Method and apparatus for classification of lesion based on learning data applying one or more augmentation methods in lesion information augmented patch of medical image
US11676273B2 (en) Methods and systems for displaying image
CN114092475B (zh) 病灶长径确定方法、图像标注方法、装置及计算机设备
CN111951272A (zh) 脑部影像的分割方法、装置、计算机设备和可读存储介质
CN115965750B (zh) 血管重建方法、装置、计算机设备和可读存储介质
CN115272250B (zh) 确定病灶位置方法、装置、计算机设备和存储介质
CN110570425B (zh) 一种基于深度强化学习算法的肺结节分析方法及装置
CN112862955B (zh) 建立三维模型的方法、装置、设备、存储介质和程序产品
CN113962958B (zh) 一种征象检测方法及装置
CN106530386B (zh) 医学图像的体绘制方法及其***
CN114723723A (zh) 医学影像处理方法、计算机设备和存储介质
US20220277175A1 (en) Method and system for training and deploying an artificial intelligence model on pre-scan converted ultrasound image data
CN114820483A (zh) 图像检测方法、装置及计算机设备
JP7105918B2 (ja) 領域特定装置、方法およびプログラム
CN115393246A (zh) 图像分割***及图像分割方法
US11308622B2 (en) Information processing apparatus and method for controlling the same to generate a difference image from first and second inspection images
EP4102456A1 (en) Calculation program, calculation method, and calculation device
US20240119592A1 (en) Information processing apparatus, information processing method, and program

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20940920

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20940920

Country of ref document: EP

Kind code of ref document: A1

122 Ep: pct application non-entry in european phase

Ref document number: 20940920

Country of ref document: EP

Kind code of ref document: A1

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205 DATED 27/06/2023)

122 Ep: pct application non-entry in european phase

Ref document number: 20940920

Country of ref document: EP

Kind code of ref document: A1