CN105118030B - The bearing calibration of medical image metal artifacts and device - Google Patents
The bearing calibration of medical image metal artifacts and device Download PDFInfo
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Abstract
The present invention relates to the metal artifacts reduction devices of medical image, including：Segmentation threshold determination unit determines the segmentation threshold of metallic region based on the gray-value variation trend between the metallic region and other regions of indirect exposure area in the medical image；Metallic region cutting unit divides the metallic region in the medical image according to the segmentation threshold；Projecting cell carries out front projection to obtain the data for projection of the metallic region to the metallic region；Updating unit, the data for projection that the metallic region is updated using the data for projection of non-metallic regions generate updated data for projection；Reconstruction unit carries out rebuilding the first reconstruction image of acquisition with the updated data for projection；Acquiring unit restores metallic region to obtain the second reconstruction image in first reconstruction image.
Description
Technical field
The present invention relates to the bearing calibration of digital image processing field more particularly to a kind of medical image metal artifacts and dresses
It sets.
Background technology
Artifact in medical image refers to the abnormal image unrelated with institutional framework generated in imaging process.Wherein,
Metal artifacts are mainly caused by the great object of density difference in human body, the metal that is implanted into such as human body：Intrauterine device, plant
The motor etc. being implanted into the patient when the steel plate, the row epilepsy electrode implant surgery that enter patient's body, the absorption coefficient of metal object
Typically tens times or more of human body most tissues absorption coefficient, so as to cause the projection of metal and tissue delivery position
Data variation is violent and discontinuous.
Fig. 1 is the mammary gland die body reconstruction image for including calcification point artifact without metal artifacts reduction.As shown in Figure 1, should
Image is the reconstruction image obtained after being rebuild to the projected image comprising metal calcification point, the metal calcification point in image
The artifact of surrounding reduces picture quality, affects interpretation of the doctor to image and the accuracy to pathological changes diagnosis.
Threshold calculations are an important rings for image partition method, and with the relevant metal artifacts of mammary gland tomographic image reconstructing
Metallic region segmentation problem is related equally in correction, the quality of metallic region segmentation result directly determines metal artifacts reduction
The quality of effect.The segmentation threshold of determining metallic region is usually required for metallic region segmentation, namely is determined and belonged to gold
Belong to the gray value interval where the pixel in region, is partitioned into the metal part in reconstruction image using the metal segmentation threshold
It is corrected, therefore the segmentation threshold of metallic region is the core of entire metal partitioning algorithm.For now, generally use pair
A certain number of image patterns are counted, and the analysis to sample are relied on, using the empirical value of acquisition as the segmentation of metallic region
Threshold value causes metallic region segmentation inaccurate, and metal artifacts reduction effect is poor.
It is, therefore, desirable to provide a kind of method of new deposit category region segmentation threshold value really, to be accurately partitioned into metal area
Domain, namely the accuracy of metallic region segmentation is improved, to effectively correct the metal artifacts in medical image.
Invention content
The problem to be solved in the present invention is to provide bearing calibration and the device of a kind of medical image metal artifacts, with accurately
The segmentation threshold for determining segmentation metallic region improves the accuracy of metallic region segmentation especially for the calcification point in breast, from
And effectively correct the metal artifacts in medical image.
In order to solve the above technical problems, the present invention provides a kind of bearing calibration of medical image metal artifacts, including：According to
Segmentation threshold determines that metallic region, the segmentation threshold are metallic regions in the indirect exposure area according to the medical image
It is obtained with the gray-value variation trend in other regions.
Optionally, gray-value variation trend be according to the corresponding pixel number of different gray value in the medical image with
Than its gray value, the ratio of the corresponding mean pixel point number of big multiple gray values obtains.
Optionally, gray-value variation trend is arrived according to the at most corresponding gray value of pixel number in the medical image
The corresponding pixel number of different gray values between maximum gradation value is corresponding with the multiple gray values bigger than its gray value flat
The ratio of equal pixel number obtains.
Optionally, the variation tendency f (i) of gray value is obtained according to following formula：
Wherein：Corresponding pixel number when being i that histogram (i) is gray value, histsum (i+1) be gray value not
The sum of pixel number corresponding less than the gray value of i+1, imaxFor maximum gradation value, the gray value that i is represented is not less than pixel
The at most corresponding gray value of number.
Optionally, in the histogram generated with the correspondence between i and f (i), the corresponding any ash in trough domain is chosen
The upper limit of the angle value as segmentation threshold；Choose lower limit of the corresponding any gray value in wave crest domain as segmentation threshold.
Optionally, in the histogram generated with the correspondence between i and f (i), the corresponding gray scale of minimum trough is chosen
It is worth the upper limit as segmentation threshold；Choose lower limit of the corresponding gray value of highest wave crest as segmentation threshold.
In order to solve the above technical problems, the present invention also provides a kind of bearing calibrations of medical image metal artifacts, including：
Divide the metallic region in the medical image according to segmentation threshold；
Front projection is carried out to obtain the data for projection of metallic region to the metallic region；
The data for projection that the metallic region is updated using the data for projection of non-metallic regions generates updated projection number
According to；
With the updated data for projection rebuild and obtains the first reconstruction image；
Restore metallic region in first reconstruction image to obtain the second reconstruction image, the segmentation of the metallic region
Threshold value is to determine that method obtains according to above-mentioned segmentation threshold.
Optionally, metallic region segmentation is carried out to the medical image by clustering procedure or iterative method.
Optionally, pass through a kind of projection to non-metallic regions in neighbor interpolation, bilinear interpolation, cubic spline interpolation
Data update the data for projection of the metallic region into row interpolation.
Optionally, the medical image is in DBT images with metal artifacts, CT images, SPECT images, PET image
One kind.
In order to solve the above technical problems, the present invention also provides a kind of means for correctings of medical image metal artifacts, including：
Segmentation threshold determination unit, the metallic region based on indirect exposure area in the medical image and other regions
Between gray-value variation trend determine the segmentation threshold of metallic region；
Metallic region cutting unit divides the metallic region in the medical image according to the segmentation threshold；
Projecting cell carries out front projection to obtain the data for projection of the metallic region to the metallic region；
Updating unit, after the data for projection generation update that the metallic region is updated using the data for projection of non-metallic regions
Data for projection；
Reconstruction unit carries out rebuilding the first reconstruction image of acquisition with the updated data for projection；
Acquiring unit restores metallic region to obtain the second reconstruction image in first reconstruction image.
Compared with prior art, the present invention is automatically true using the gray-value variation trend between metallic region and other regions
The upper threshold and bottom threshold of the fixed metallic region, simply and effectively determine the segmentation threshold of metallic region, avoid existing
Have and excessively rely on sample properties in technology, the influence of artificial subjective factor, is accorded with during manual setting segmentation threshold
Close the reconstruction image of actual clinical demand.Especially to the metal artifacts that are formed due to metal calcification point in correction mammary gland have compared with
Good calibration result, the galactophore image after correction meet actual clinical demand.
Description of the drawings
Fig. 1 is the not corrected mammary gland die body reconstruction image with calcification point artifact；
Fig. 2 is the grey level histogram of the reconstruction image of the embodiment of the present invention；
Fig. 3 is the gray value ratio figure of the embodiment of the present invention；
Fig. 4 is the flow chart of deposit category cut zone really of the embodiment of the present invention；
Fig. 5 is the flow chart that front projection is carried out to metallic region of the embodiment of the present invention；
Fig. 6 is the structural schematic diagram of the metal artifacts reduction device of the embodiment of the present invention.
Specific implementation mode
To make the above purposes, features and advantages of the invention more obvious and understandable, below in conjunction with the accompanying drawings to the present invention
Specific implementation mode be described in detail.Detail is elaborated in the following description in order to fully understand the present invention.But
It is the present invention with a variety of to implement different from other manner described here, those skilled in the art can be without prejudice to originally
Similar popularization is done in the case of invention intension.Therefore the present invention is not limited by following public specific implementation mode.
The metal artifacts reduction method of the present invention, can be to digital galactophore tomography (DBT, Digital Breast
Tomosynthesis) image, computed tomography (CT, Computed Tomography) image, single photon emission calculate
Machine tomography (SPECT, Single-Photon Emission Computed Tomography) image, positron emission fault
There is (PET, Positron Emission Tomography, PET) image etc. the medical image of metal artifacts to be corrected, with
Under by taking medical image to be corrected is DBT images as an example, be in particular with the mammary gland tomographic image of metal calcification point
Technical scheme of the present invention is described in detail in example.
The bearing calibration of the medical image metal artifacts of the present embodiment includes：
Step 1, segmentation threshold is obtained, the medical image is split to obtain metal area according to the segmentation threshold
Domain；
Step 2, front projection is carried out to obtain the front projection data of the metallic region to the metallic region；
Step 3, the data for projection generation that the metallic region is updated using the data for projection of non-metallic regions is updated
Data for projection；
Step 4, with the updated data for projection rebuild and obtain the first reconstruction image；
Step 5, restore metallic region in first reconstruction image to obtain the second reconstruction image.
In above-mentioned steps, segmentation threshold obtains in the present embodiment mainly according to the indirect exposure region of medical image
Gray-value variation trend in domain between metallic region and other regions obtains.This is because as shown in Figure 1, metal dots are in weight
It builds in image, shows to be highlighted in gray scale, and gray value is also Relatively centralized.Therefore in order to described in more accurate acquisition
Segmentation threshold obtains the gray-value variation between the metallic region and other regions in the present embodiment especially by following manner
Trend.
The process for obtaining metallic region segmentation threshold is described in detail below with reference to Fig. 2, Fig. 3.Fig. 2 a are according to institute
The first grey level histogram that the area-of-interest in the indirect exposure area of medical image acquires is stated, wherein abscissa is
Gray value, ordinate are pixel number.The at most corresponding gray value of selected pixels point number is in the first grey level histogram
i；Only retain the part that gray value is more than or equal to i, the second grey level histogram (with reference to figure 2b) is generated, further according to the second intensity histogram
The member-retaining portion of figure obtains f (i) with formula 1, and reflection gray value is generated more than or equal to i's with the correspondence between i and f (i)
Area grayscale value overall variation tendency chart.
Wherein：Corresponding pixel number when being i that histogram (i) is gray value, histsum (i+1) be gray value not
The sum of pixel number corresponding less than the gray value of i+1, imaxFor maximum gradation value, the gray value that i is represented is not less than pixel
The at most corresponding gray value of number.
In formula 1：histsum(i+1)/(imax- i) it is the gray value mean pixel number bigger than gray value i, in other words
It is that the summation of the corresponding pixel number of the gray value bigger than gray value i is averaged pixel corresponding on each gray value
Number.Therefore the meaning of formula 1 is the corresponding pixel numbers of current grayvalue i and the gray value mean pixel bigger than gray value i
The ratio of number.It shows to be highlighted in gray scale because of metal or calcification point, and gray value is also Relatively centralized, therefore can be with
The upper and lower bound of metallic region segmentation threshold is found by (wave crest, trough) at the mutation of this ratio, and then isolates calcification
Point.
The upper limit (hereinafter referred to as Ta) of the corresponding gray value of minimum trough as segmentation threshold is chosen in figure 3；It chooses
Lower limit (hereinafter referred to as Tb) of the corresponding gray value of highest wave crest as segmentation threshold.Optionally, the Ta and Tb be not necessarily
It is minimum trough and the corresponding gray value of highest wave crest in Fig. 3, can is near trough domain and the wave crest near trough
Any gray value in wave crest domain.Such as, Tb can be the gray area between the gray value corresponding to highest wave crest and minimum trough
Between 1/2 at lean in gray scale interval between gray value corresponding to corresponding gray value or highest wave crest and minimum trough
At 1/4 gray scale interval of nearly highest wave crest or 1/3 the corresponding gray value in gray scale interval place, Ta can be minimum trough pair
The gray value answered；Or Tb can be the corresponding gray value of highest wave crest, Ta can be corresponding to highest wave crest and minimum trough
Gray value between gray scale interval 1/2 at corresponding gray value or highest wave crest and the gray scale corresponding to minimum trough
In gray scale interval between value close to minimum trough 1/4 gray scale interval at or 1/3 the corresponding gray scale in gray scale interval place
Value.In practical applications, the boundary of calcification point region can also be more accurately determined by adjusting Ta and Tb.
In other embodiments, the gray-value variation trend between the metallic region and other regions can be according to described
The corresponding pixel number of difference gray value mean pixel corresponding with multiple gray values bigger than its gray value in medical image
The ratio of point number obtains, and the corresponding mean pixel point number of the multiple gray value refers to the corresponding pixel of multiple gray values
The summation of number is averaged pixel number corresponding on each gray value.
Fig. 4 is to determine the iterative algorithm flow chart of cut zone, below with reference to attached drawing 4 to using Ta, Tb, determine metal/
The process of calcification point cut zone is described in detail.The process is by the pixel to having determined as metal cut zone
Point is iterated to obtain metal cut zone.Alternatively, it is also possible to using clustering procedure.
Such as Fig. 4, S11：It is given birth to using pixel of the grey scale pixel value in collected reconstruction image higher than Ta as initial seed
At initial seed set；
S12：Judge whether the seed point in initial seed set all used, just terminates if all seed points all used
Operation executes S13 if having seed point not use；
S13:To be judged as that no used seed point generates independent seed subset in S12, S14 is executed；
S14:Judge whether the seed point in seed subset all used, is executed if the seed point in seed subset is not used
S15, if the seed point in seed subset all used execution S16；
S15：The gray value of all seed points pixel in its 3 × 3 × 3 neighborhood is higher than the picture of Tb in nodes for research subset
Vegetarian refreshments；
S16：According to the seed point that seed subset includes, judge whether the seed point in the seed subset is formed by region
For effective metal region, if it is metallic region then to determine that the seed subset is formed by region, if otherwise giving up the seed
Subset.Since common metal region will present specific shape, in the present embodiment specifically, if the set institute of seed point
The region of formation, which meets the shape, can be considered metallic region, be judged as non-metallic regions if shape difference is larger.
S17：It is higher than the pixel and S13 of Tb to the gray value in 3 × 3 × 3 neighborhood of all seed points that is searched in S15
Initial subset in pixel compared, if the gray value searched higher than Tb pixel and S13 seed subset in
Seed point it is identical, i.e., not new pixel generates, then returns and execute S14, if the gray value searched is higher than the pixel of Tb
Point is different from the seed point in the seed subset of S13, that is, has new pixel to generate, then returns and execute S13, while also returning to and holding
Row S18；
S18：It is judged as finding out the pixel belonged in S11 in initial seed set in new pixel in S17, and
The pixel is deleted in initial seed set.
It is to find new seed point by the way that 3 × 3 × 3 neighborhoods in seed point are interior it should be noted that in the present embodiment,
It in other embodiments can also be by being found in 2 × 2 × 2 neighborhoods of seed point, specifically in much neighborhoods of seed point
Finding seed point can be depending on actual demand.
Fig. 5 is the flow chart that front projection is carried out to metallic region of the embodiment of the present invention.5 pairs of metal areas referring to the drawings
The process of the front projection in domain illustrates.
S21：The metal/Ca point set of acquisition is determined as cut zone (metallic region)；
S22：The pixel of the S21 cut zone determined is marked；
S23：Front projection is carried out to cut zone according to corresponding geometric parameter；The purpose of the step is that metallic region is mapped to
On the corresponding position of projected image, so-called projected image is to generate the projected image obtained by detector before medical image.
S24：Part to exceeding detector regimes in the result of front projection is marked, and is retained in detector regimes
Data (can be used) when there was only data reconstruction on the detector；
S25：To the metal part label on the data for projection of S24, and template is generated, is mainly used for marking metal portion
Point.
In above-mentioned steps by carrying out front projection to metallic region after, need to update by the data for projection of non-metallic regions
The data for projection of the metallic region, the data for projection of the non-metallic regions refer to before generating the medical image by detector
The data for projection of the non-metallic regions of acquisition.Specifically, the projection to the metallic region in the following manner in the present embodiment
Data are updated.
According to pixel in metallic region projected image (refer to before obtaining medical image, the throwing that is obtained by detector
Shadow image) in position choose the pixel of its neighborhood, using its neighborhood pixel gray value to the picture of metallic region
Vegetarian refreshments updates the data for projection of metallic region into row interpolation.In practical renewal process, updated by way of from outside to inside
The data for projection of the metallic region.Namely first the boundary part of update metallic region then progressive updating to metallic region
It is internal.
In the present embodiment, metal pixel point is selected by judging different location of the metal pixel point in projected image
Pixel number around neighborhood.In general, when the pixel of metallic region is located at 4 vertex of projected image, Ke Yixuan
The pixel of 3 non-metallic regions of its neighborhood is into row interpolation；The pixel of metallic region is located on 4 sides of projected image
When, the pixels of 5 non-metallic regions of its neighborhood can be selected into row interpolation；The pixel of metallic region, which is located at, removes above two
When other positions outside situation, 8 pixels of its neighborhood can be selected into row interpolation.Using the side being averaged in the present embodiment
Formula is into row interpolation, that is to say, that the gray value of the pixel of metallic region is the average value of the gray value of the pixel of its neighborhood.
In practical application, when to the pixel for metal is marked into row interpolation, the neighborhood of pixel points of metallic region has
Effect pixel (pixels of non-metallic regions) not necessarily meets above-mentioned situation, at this point for positioned at 4 sides of projected image
On metallic region pixel for, as long as meet its neighborhood effective pixel points be more than or equal to 3 with this effectively
Pixel updates the data for projection of the metal pixel point into row interpolation, with this 4 valid pixels if effective pixel points are 4
It puts to update the data for projection of metal pixel point；And in the case of the pixel of metallic region is among projected image and
Speech, as long as the effective pixel points for meeting its neighborhood update this with the effective pixel points more than or equal to 4 into row interpolation
The data for projection of metal pixel point.When being updated to the data for projection of the pixel of metallic region using computer program,
It can be carried out by way of cycle, establish and recycle and be arranged the condition that cycle carries out, thrown when the pixel of metallic region is located at
Its neighborhood effective pixel points number is not less than 3, when the pixel of metallic region is located at the side of projected image when the vertex of shadow image
Not less than 3, when the pixel of metallic region is located among projected image, its neighborhood has its neighborhood effective pixel points number when upper
It imitates when pixel number is not less than 4 just into row interpolation, otherwise skips this pixel, first other pixels are operated, work as institute
End loop and result is exported when having the pixel labeled as metallic region to complete respective handling.
It is above-mentioned by way of taking the average value of effective pixel points neighbor interpolation for have updated the projection of metallic region
Data, can also use in other embodiments bilinear interpolation, cubic spline interpolation etc. to the data for projection of metallic region into
Row update.
With the projection number of the data for projection and the non-metallic regions of the updated metallic region obtained in above-mentioned steps
The first reconstruction image is obtained according to rebuild；Metallic region is removed due to being equivalent in the data of this reconstruction, this
There is no metal artifacts in the first reconstruction image that reconstruction obtains.It is not deposited in the first reconstruction image obtained due to this reconstruction
In the information of metallic region, therefore, it is also desirable to restore original metallic region in the first reconstruction image, specifically, will divide
The metal/Ca point (metallic region) gone out puts back to the position where its in the first reconstruction image, generates the second of the pseudo- movie queen of removal
Reconstruction image.
Fig. 6 is the structural schematic diagram of the means for correcting of the medical image metal artifacts of the embodiment of the present invention, referring to attached
The means for correcting of medical image metal artifacts is described in detail in Fig. 6.The device includes：Segmentation threshold determination unit 11, base
Gray-value variation trend in the medical image between the metallic region of indirect exposure area and other regions determines gold
Belong to the segmentation threshold in region；Metallic region cutting unit 12 divides the metal in the medical image according to the segmentation threshold
Region；Projecting cell 13 carries out front projection to obtain the data for projection of the metallic region to the metallic region；Updating unit
14, the data for projection that the metallic region is updated using the data for projection of non-metallic regions generates updated data for projection；Weight
Unit 15 is built, carries out rebuilding the first reconstruction image of acquisition with the updated data for projection；Acquiring unit 16, described first
Restore metallic region in reconstruction image to obtain the second reconstruction image.
The bearing calibration that the course of work of the means for correcting of above-mentioned metal artifacts may refer to metal artifacts carries out, herein not
It repeats again.
Although the invention has been described by way of example and in terms of the preferred embodiments, but it is not for limiting the present invention, any this field
Technical staff without departing from the spirit and scope of the present invention, may be by the methods and technical content of the disclosure above to this hair
Bright technical solution makes possible variation and modification, therefore, every content without departing from technical solution of the present invention, and according to the present invention
Technical spirit to any simple modifications, equivalents, and modifications made by above example, belong to technical solution of the present invention
Protection domain.
Claims (9)
1. a kind of bearing calibration of medical image metal artifacts, which is characterized in that including：Metal area is determined according to segmentation threshold
Domain, the segmentation threshold are the gray values of metallic region and other regions in the indirect exposure area according to the medical image
Variation tendency obtains；The gray-value variation trend is according to the corresponding pixel number of different gray values in the medical image
The ratio of mean pixel point number corresponding with multiple gray values bigger than its gray value obtains.
2. the bearing calibration of medical image metal artifacts according to claim 1, which is characterized in that gray-value variation trend
It is according to the at most corresponding gray value of pixel number in the medical image to the different gray values pair between maximum gradation value
The ratio of the pixel number answered mean pixel point number corresponding with multiple gray values bigger than its gray value obtains.
3. the bearing calibration of medical image metal artifacts according to claim 2, which is characterized in that the variation of gray value becomes
Gesture f (i) is obtained according to following formula：
Wherein：Histogram (i) is gray value when being i, and corresponding pixel number, histsum (i+1) are not less than for gray value
The sum of corresponding pixel number of gray value of i+1, imaxFor maximum gradation value, the gray value that i is represented is not less than pixel number
At most corresponding gray value.
4. the bearing calibration of medical image metal artifacts according to claim 3, which is characterized in that with i and f (i) it
Between the histogram that generates of correspondence in, choose the upper limit of the corresponding any gray value in trough domain as segmentation threshold；It chooses
Lower limit of the corresponding any gray value in wave crest domain as segmentation threshold.
5. the bearing calibration of medical image metal artifacts according to claim 3, which is characterized in that with i and f (i) it
Between correspondence generate histogram in, choose the upper limit of the corresponding gray value of minimum trough as segmentation threshold；It chooses most
Lower limit of the corresponding gray value of high wave crest as segmentation threshold.
6. a kind of bearing calibration of medical image metal artifacts, which is characterized in that including：
Divide the metallic region in the medical image according to segmentation threshold；
Front projection is carried out to obtain the data for projection of metallic region to the metallic region；
The data for projection that the metallic region is updated using the data for projection of non-metallic regions generates updated data for projection；
With the updated data for projection rebuild and obtains the first reconstruction image；
Restore metallic region in first reconstruction image to obtain the second reconstruction image, which is characterized in that the metal area
The segmentation threshold in domain is obtained according to the determination method of any one of claim 1 to 5 segmentation threshold.
7. the bearing calibration of medical image metal artifacts according to claim 6, which is characterized in that by clustering procedure or repeatedly
Metallic region segmentation is carried out to the medical image for method.
8. the bearing calibration of medical image metal artifacts according to claim 6, which is characterized in that by neighbor interpolation,
A kind of data for projection to non-metallic regions in bilinear interpolation, cubic spline interpolation updates the metal area into row interpolation
The data for projection in domain.
9. a kind of means for correcting of medical image metal artifacts, which is characterized in that including：
Segmentation threshold determination unit, between metallic region and other regions based on indirect exposure area in the medical image
Gray-value variation trend determine the segmentation threshold of metallic region, the gray-value variation trend is according in the medical image
The ratio of the corresponding pixel number of different gray values and the corresponding mean pixel point number of the multiple gray values bigger than its gray value
Value obtains；
Metallic region cutting unit divides the metallic region in the medical image according to the segmentation threshold；
Projecting cell carries out front projection to obtain the data for projection of the metallic region to the metallic region；
Updating unit, the data for projection that the metallic region is updated using the data for projection of non-metallic regions generate updated throwing
Shadow data；
Reconstruction unit carries out rebuilding the first reconstruction image of acquisition with the updated data for projection；
Acquiring unit restores metallic region to obtain the second reconstruction image in first reconstruction image.
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| CN107730569B (en) * | 2017-11-30 | 2021-01-01 | 上海联影医疗科技股份有限公司 | Medical image artifact correction method and device |
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| CN109949243B (en) * | 2019-03-20 | 2021-05-11 | 数坤（北京）网络科技有限公司 | Calcification artifact eliminating method and device and computer storage medium |
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