CN105118030A - Medical image metal artifact correction method and device - Google Patents
Medical image metal artifact correction method and device Download PDFInfo
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Abstract
The present invention relates to a medical image metal artifact correction device which comprises a segmentation threshold determining unit which determines the segmentation threshold of a metal area based on the gray value change trend between the metal area of a non direct exposure area in a medical image and other areas, a metal area segmentation unit which divides the metal area in the medical image according to the segmentation threshold, a projection unit which carries out front projection for the metal area to obtain the projection data of the metal area, an updating unit which using the projection data of a non-metal area to update the projection data of the metal area and generate updated projection data, a reconstruction unit which carries out reconstruction with the updated projected data to obtain a first reconstruction image, and an obtaining unit which restores the metal area in the first reconstruction image to obtain a second reconstruction image.
Description
Technical field
The present invention relates to digital image processing field, particularly relate to a kind of bearing calibration and device of medical image metal artifacts.
Background technology
Artifact in medical image refers to the abnormal image irrelevant with institutional framework produced in imaging process.Wherein, metal artifacts mainly causes due to the great object of density difference in human body, metal as people's et al. Ke: the motor the etc. when steel plate in intrauterine device, patients with implantation body, the electrode implant surgery of row epilepsy in patients with implantation body, more than tens times of the absorption coefficient of metal object normally human body most tissues absorption coefficient, thus it is violent and discontinuous to cause the data for projection of metal and tissue delivery position to change.
Fig. 1 rebuilds image without the mammary gland die body comprising calcification point artifact of metal artifacts reduction.As shown in Figure 1, this image is the reconstruction image obtained after rebuilding the projected image comprising metal calcification point, and the artifact of the metal calcification point surrounding in image reduces picture quality, have impact on the interpretation of doctor to image and the accuracy to pathological changes diagnosis.
Threshold calculations is an important ring of image partition method, and in the metal artifacts reduction relevant with mammary gland tomographic image reconstructing the same metallic region segmentation problem that relates to, the quality of metallic region segmentation result directly determines the quality of metal artifacts reduction effect.Usually the segmentation threshold determining metallic region is needed for metallic region segmentation, also the gray value interval at the pixel place belonging to metallic region is namely determined, utilize this metal segmentation threshold to be split by the metallic member rebuild in image to correct, therefore the segmentation threshold of metallic region is the core of whole metal partitioning algorithm.For now, usually adopt and add up the image pattern of some, rely on the analysis to sample, using the segmentation threshold of the empirical value of acquisition as metallic region, cause metallic region to be split inaccurate, metal artifacts reduction effect is poor.
Therefore, need to propose a kind of new deposit really and belong to the method for region segmentation threshold value, to be partitioned into metallic region exactly, also namely improve the accuracy of metallic region segmentation, thus effectively correct the metal artifacts in medical image.
Summary of the invention
The problem to be solved in the present invention is to provide a kind of bearing calibration and device of medical image metal artifacts, to determine the segmentation threshold splitting metallic region exactly, especially for the calcification point in breast, improve the accuracy of metallic region segmentation, thus effectively correct the metal artifacts in medical image.
For solving the problems of the technologies described above, the invention provides a kind of bearing calibration of medical image metal artifacts, comprise: according to segmentation threshold determination metallic region, described segmentation threshold is that the gray-value variation trend in metallic region and other regions in the non-immediate exposure area according to described medical image obtains.
Optionally, gray-value variation trend obtains according to the ratio of the pixel number that gray-scale value different in described medical image the is corresponding mean pixel point number corresponding with multiple gray-scale values larger than its gray-scale value.
Optionally, gray-value variation trend obtains according to the ratio of the at most corresponding gray-scale value of pixel number in described medical image to the pixel number that the different gray-scale values between maximum gradation value the are corresponding mean pixel point number corresponding with multiple gray-scale values larger than its gray-scale value.
Optionally, the variation tendency f (i) of gray-scale value obtains according to following formula:
Wherein: histogram (i) is pixel number corresponding when gray-scale value is i, histsum (i+1) is not less than pixel number sum corresponding to the gray-scale value of i+1, i for gray-scale value
maxfor maximum gradation value, the gray-scale value of i representative is not less than pixel number gray-scale value corresponding at most.
Optionally, in the histogram generated with the corresponding relation between i and f (i), the upper limit of arbitrary gray-scale value corresponding to trough territory as segmentation threshold is chosen; Choose the lower limit of arbitrary gray-scale value corresponding to crest territory as segmentation threshold.
Optionally, in the histogram generated with the corresponding relation between i and f (i), the upper limit of gray-scale value corresponding to minimum trough as segmentation threshold is chosen; Choose the lower limit of gray-scale value corresponding to the highest crest as segmentation threshold.
For solving the problems of the technologies described above, the present invention also provides a kind of bearing calibration of medical image metal artifacts, comprising:
The metallic region in described medical image is split according to segmentation threshold;
Front projection is carried out to obtain the data for projection of metallic region to described metallic region;
The data for projection adopting the data for projection of non-metallic regions to upgrade described metallic region generates the data for projection after upgrading;
Carry out reconstruction acquisition first with the data for projection after described renewal and rebuild image;
Rebuild in image described first and recover metallic region to obtain the second reconstruction image, the segmentation threshold of described metallic region obtains according to above-mentioned segmentation threshold defining method.
Optionally, by clustering procedure or process of iteration, metallic region segmentation is carried out to described medical image.
Optionally, interpolation is carried out to upgrade the data for projection of described metallic region by a kind of data for projection to non-metallic regions in neighbor interpolation, bilinear interpolation, cubic spline interpolation.
Optionally, described medical image is have the one in the DBT image of metal artifacts, CT image, SPECT image, PET image.
For solving the problems of the technologies described above, the present invention also provides a kind of means for correcting of medical image metal artifacts, comprising:
Segmentation threshold determining unit, based on the segmentation threshold of the gray-value variation trend determination metallic region between the metallic region of non-immediate exposure area in described medical image and other regions;
Metallic region cutting unit, splits the metallic region in described medical image according to described segmentation threshold;
Projecting cell, carries out front projection to obtain the data for projection of described metallic region to described metallic region;
Updating block, the data for projection adopting the data for projection of non-metallic regions to upgrade described metallic region generates the data for projection after upgrading;
Reconstruction unit, carries out reconstruction acquisition first with the data for projection after described renewal and rebuilds image;
Acquiring unit, rebuilds in image described first and recovers metallic region to obtain the second reconstruction image.
Compared with prior art, the present invention utilizes the gray-value variation trend between metallic region and other regions automatically to determine upper threshold and the bottom threshold of described metallic region, the simple and effective segmentation threshold determining metallic region, avoid in prior art and too rely on sample properties, the impact of subjective factor artificial in segmentation threshold process is manually set, obtains the reconstruction image of realistic clinical demand.Especially there is good calibration result, the clinical demand that the galactophore image after correction is realistic to correcting the metal artifacts formed due to metal calcification point in mammary gland.
Accompanying drawing explanation
Fig. 1 is that the not calibrated mammary gland die body with calcification point artifact rebuilds image;
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 be the embodiment of the present invention really deposit belong to the process flow diagram of cut zone;
Fig. 5 is process flow diagram metallic region being carried out to front projection of the embodiment of the present invention;
Fig. 6 is the structural representation of the metal artifacts reduction device of the embodiment of the present invention.
Embodiment
For enabling above-mentioned purpose of the present invention, feature and advantage more become apparent, and are described in detail the specific embodiment of the present invention below in conjunction with accompanying drawing.Set forth detail in the following description so that fully understand the present invention.But the present invention can be different from alternate manner described here to implement with multiple, those skilled in the art can when without prejudice to doing similar popularization when intension of the present invention.Therefore the present invention is not by the restriction of following public embodiment.
Metal artifacts reduction method of the present invention, can to digital galactophore tomography (DBT, DigitalBreastTomosynthesis) image, computed tomography (CT, ComputedTomography) image, single photon emission computed tomography (SPECT, Single-PhotonEmissionComputedTomography) image, positron emission fault (PET, PositronEmissionTomography, PET) medical image that image etc. have a metal artifacts corrects, below for medical image to be corrected for DBT image, being in particular the mammary gland tomographic image with metal calcification point is that example is described in detail technical scheme of the present invention.
The bearing calibration of the medical image metal artifacts of the present embodiment comprises:
Step 1, obtains segmentation threshold, splits to obtain metallic region according to described segmentation threshold to described medical image;
Step 2, carries out front projection to obtain the front projection data of described metallic region to described metallic region;
Step 3, the data for projection adopting the data for projection of non-metallic regions to upgrade described metallic region generates the data for projection after upgrading;
Step 4, carries out reconstruction acquisition first with the data for projection after described renewal and rebuilds image;
Step 5, rebuilds in image described first and recovers metallic region to obtain the second reconstruction image.
In above-mentioned steps, mainly obtain according to the gray-value variation trend in the non-immediate exposure area of medical image between metallic region and other regions in acquisition the present embodiment of segmentation threshold.This is because as shown in Figure 1, metal dots is in reconstruction image, and it is highlighted that gray scale shows, and gray-scale value is also Relatively centralized.Therefore in order to obtain described segmentation threshold more accurately, especially by the gray-value variation trend obtained with under type between described metallic region and other regions in the present embodiment.
Below with reference to Fig. 2, Fig. 3, the process obtaining metallic region segmentation threshold is described in detail.Fig. 2 a is the first grey level histogram acquired according to the area-of-interest in the non-immediate exposure area of described medical image, and wherein horizontal ordinate is gray-scale value, and ordinate is pixel number.In the first grey level histogram, the corresponding at most gray-scale value of selected pixels point number is i; Only retain the part that gray-scale value is more than or equal to i, generate the second grey level histogram (with reference to figure 2b), obtain f (i) according to the reserve part formula 1 of the second grey level histogram again, generate with the corresponding relation between i and f (i) and reflect that gray-scale value is more than or equal to the area grayscale value overall variation trend map of i.
Wherein: histogram (i) is pixel number corresponding when gray-scale value is i, histsum (i+1) is not less than pixel number sum corresponding to the gray-scale value of i+1, i for gray-scale value
maxfor maximum gradation value, the gray-scale value of i representative is not less than pixel number gray-scale value corresponding at most.
In formula 1: histsum (i+1)/(i
max-i) be the gray-scale value mean pixel number larger than gray-scale value i, or perhaps the pixel number that the summation of pixel number corresponding to the gray-scale value larger than gray-scale value i is on average corresponding on each gray-scale value.Therefore the implication of formula 1 is pixel number that current grayvalue i is corresponding and the ratio of the gray-scale value mean pixel number larger than this gray-scale value i.Be highlighted because metal or calcification point show in gray scale, and gray-scale value is also Relatively centralized, therefore the upper and lower bound of metallic region segmentation threshold can be found by sudden change place (crest, trough) of this ratio, and then isolate calcification point.
Choose the upper limit (hereinafter referred to as Ta) of gray-scale value corresponding to minimum trough as segmentation threshold in figure 3; Choose the lower limit (hereinafter referred to as Tb) of gray-scale value corresponding to the highest crest as segmentation threshold.Optionally, described Ta and Tb not necessarily in Fig. 3 minimum trough and the highest crest corresponding gray-scale value, can be the arbitrary gray-scale value in the trough territory near trough and the crest territory near crest.As, Tb can be gray-scale value corresponding to 1/2 place between the gray area between the highest crest with the gray-scale value corresponding to minimum trough, or between the gray area between the highest crest with the gray-scale value corresponding to minimum trough near between the gray area of 1/4 of the highest crest place or 1/3 gray area between gray-scale value corresponding to place, Ta can be the gray-scale value that minimum trough is corresponding; Or Tb can be the gray-scale value that the highest crest is corresponding, Ta can be gray-scale value corresponding to 1/2 place between the gray area between the highest crest with the gray-scale value corresponding to minimum trough, or between the gray area between the highest crest with the gray-scale value corresponding to minimum trough near between the gray area of 1/4 of minimum trough place or 1/3 gray area between gray-scale value corresponding to place.In actual applications, also can come to determine more accurately by adjustment Ta and Tb the border of calcification point region.
In other embodiments, gray-value variation trend between described metallic region from other regions can be corresponding with the multiple gray-scale values larger than its gray-scale value according to the pixel number that gray-scale value different in described medical image is corresponding the ratio of mean pixel point number obtain, the mean pixel point number that described multiple gray-scale value is corresponding refers to the pixel number that the summation of the pixel number that multiple gray-scale value is corresponding is on average corresponding on each gray-scale value.
Fig. 4 is the iterative algorithm process flow diagram determining cut zone, below with reference to accompanying drawing 4 to utilizing Ta, Tb, determining that the process of metal/Ca point cut zone is described in detail.Described process obtains metal cut zone by carrying out iteration to the pixel being defined as metal cut zone.Selectively, also clustering procedure can be adopted.
As Fig. 4, S11: grey scale pixel value in the reconstruction image that collects is generated initial seed set higher than the pixel of Ta as initial seed;
S12: judge whether the Seed Points in initial seed set all used, if all Seed Points all used just terminate computing, if there is Seed Points not use, perform S13;
S13: to be judged as not having used Seed Points to generate independently seed subset in S12, performs S14;
S14: judge whether the Seed Points in seed subset all used, if the Seed Points in seed subset does not use perform S15, if the Seed Points in seed subset all used execution S16;
S15: in nodes for research subset, the gray-scale value of all Seed Points pixel in its 3 × 3 × 3 neighborhood is higher than the pixel of Tb;
S16: the Seed Points comprised according to seed subset, judges whether the region that the Seed Points in this seed subset is formed is effective metal region, if then determine that the region that described seed subset is formed is metallic region, then gives up this seed subset if not., if this shape of region conforms that the set of Seed Points is formed just can think metallic region, if shape difference is comparatively large, be judged as non-metallic regions because common metal region can present specific shape, therefore, in the present embodiment particularly.
S17: the gray-scale value in all Seed Points 3 × 3 × 3 neighborhood searched in S15 is contrasted higher than the pixel in the pixel of Tb and the initial subset of S13, if the gray-scale value searched is identical higher than the Seed Points in the pixel of Tb and the seed subset of S13, namely new pixel is not had to produce, then return and perform S14, if the gray-scale value searched is different higher than the Seed Points in the pixel of Tb and the seed subset of S13, namely new pixel is had to produce, then return and perform S13, also return simultaneously and perform S18;
S18: be judged as finding out the pixel belonged in S11 in initial seed set in new pixel in S17, and delete this pixel in initial seed set.
It should be noted that, in the present embodiment, by finding new Seed Points in 3 × 3 × 3 neighborhoods of Seed Points, in other embodiments also can by finding in 2 × 2 × 2 neighborhoods of Seed Points, specifically in much neighborhoods of Seed Points, finding Seed Points can determine according to the actual requirements.
Fig. 5 is process flow diagram metallic region being carried out to front projection of the embodiment of the present invention.Process referring to the front projection of accompanying drawing 5 pairs of metallic region is described.
S21: the described metal/Ca point set obtained is defined as cut zone (metallic region);
S22: the pixel of the cut zone that S21 determines is marked;
S23: front projection is carried out to cut zone according to corresponding geometric parameter; The object of this step is that metallic region is mapped on the relevant position of projected image, and so-called projected image is the projected image obtained by detector before generating medical image.
S24: exceed the portion markings of detector regimes out in the result of front projection, and retain the data (only have during data reconstruction on the detector and can use) in detector regimes;
S25: to the metallic member mark on the data for projection of S24, and plate is touched in generation, is mainly used for marking metallic member.
By after carrying out front projection to metallic region in above-mentioned steps, need the data for projection being upgraded described metallic region by the data for projection of non-metallic regions, the data for projection of the non-metallic regions that the data for projection of described non-metallic regions is obtained by detector before referring to and generating described medical image.Particularly, in the following manner the data for projection of described metallic region is upgraded in the present embodiment.
(refer to before acquisition medical image according to pixel in metallic region at projected image, the projected image obtained by detector) in position to choose the pixel of its neighborhood, utilize the gray-scale value of the pixel of its neighborhood to carry out interpolation to upgrade the data for projection of metallic region to the pixel of metallic region.In actual renewal process, it is the data for projection being upgraded described metallic region by mode from outside to inside.The boundary member namely first upgrading metallic region then progressive updating to the inside of metallic region.
In the present embodiment, by judging that diverse location that metal pixel point is arranged in projected image selects the pixel number around metal pixel vertex neighborhood.In general, when the pixel of metallic region is positioned at 4 summits of projected image, the pixel of 3 non-metallic regions of its neighborhood can be selected to carry out interpolation; When the pixel of metallic region is positioned on 4 limits of projected image, the pixel of 5 non-metallic regions of its neighborhood can be selected to carry out interpolation; When the pixel of metallic region is positioned at other positions except above-mentioned two situations, 8 pixels of its neighborhood can be selected to carry out interpolation.Adopt the mode of averaging to carry out interpolation in the present embodiment, that is the gray-scale value of the pixel of metallic region is the mean value of the gray-scale value of the pixel of its neighborhood.
In practical application, to marked for the pixel of metal carry out interpolation time, the effective pixel points (pixel of non-metallic regions) of the neighborhood of pixel points of metallic region not necessarily meets above-mentioned situation, now for the pixel of the metallic region on 4 limits being positioned at projected image, as long as the effective pixel points meeting its neighborhood is more than or equal to 3 just can carry out by this effective pixel points the data for projection that interpolation upgrades this metal pixel point, as effective pixel points be 4 then with these 4 effective pixel points to upgrade the data for projection of metal pixel point; And be positioned at for the situation in the middle of projected image for the pixel of metallic region, as long as the effective pixel points meeting its neighborhood is more than or equal to 4 just can carry out by this effective pixel points the data for projection that interpolation upgrades this metal pixel point.When adopting the data for projection of computer program to the pixel of metallic region to upgrade, can be undertaken by the mode of circulation, set up and circulate and the condition circulating and carry out is set, when the pixel of metallic region is positioned at the summit of projected image, its neighborhood effective pixel points number is not less than 3, when the pixel of metallic region is positioned on the limit of projected image, its neighborhood effective pixel points number is not less than 3, just interpolation is carried out when its neighborhood effective pixel points number is not less than 4 when the pixel of metallic region is positioned in the middle of projected image, otherwise skip this pixel, first other pixel is operated, the end loop when all pixels being labeled as metallic region all complete respective handling Output rusults.
The above-mentioned mode for the mean value being got effective pixel points by neighbor interpolation have updated the data for projection of metallic region, and the data for projection to metallic region such as bilinear interpolation, cubic spline interpolation can also be adopted in other embodiments to upgrade.
Carry out reconstruction acquisition first with the data for projection of the data for projection of the metallic region after the renewal obtained in above-mentioned steps and described non-metallic regions and rebuild image; Be equivalent to metallic region to remove due in the data that this is rebuild, therefore this is rebuild in the first reconstruction image obtained and can not there is metal artifacts.Because this rebuilds the information that there is not metallic region in the first reconstruction image obtained, therefore, also need to rebuild in image first to recover original metallic region, particularly, the metal/Ca point (metallic region) be partitioned into is put back to the position at its place in the first reconstruction image, generate the second reconstruction image removing pseudo-movie queen.
Fig. 6 is the structural representation of the means for correcting of the medical image metal artifacts of the embodiment of the present invention, and the means for correcting referring to accompanying drawing 6 pairs of medical image metal artifacts is described in detail.This device comprises: segmentation threshold determining unit 11, based on the segmentation threshold of the gray-value variation trend determination metallic region between the metallic region of non-immediate exposure area in described medical image and other regions; Metallic region cutting unit 12, splits the metallic region in described medical image according to described segmentation threshold; Projecting cell 13, carries out front projection to obtain the data for projection of described metallic region to described metallic region; Updating block 14, the data for projection adopting the data for projection of non-metallic regions to upgrade described metallic region generates the data for projection after upgrading; Reconstruction unit 15, carries out reconstruction acquisition first with the data for projection after described renewal and rebuilds image; Acquiring unit 16, rebuilds in image described first and recovers metallic region to obtain the second reconstruction image.
The course of work of the means for correcting of above-mentioned metal artifacts can be carried out see the bearing calibration of metal artifacts, repeats no more herein.
Although the present invention with preferred embodiment openly as above; but it is not for limiting the present invention; any those skilled in the art without departing from the spirit and scope of the present invention; the Method and Technology content of above-mentioned announcement can be utilized to make possible variation and amendment to technical solution of the present invention; therefore; every content not departing from technical solution of the present invention; the any simple modification done above embodiment according to technical spirit of the present invention, equivalent variations and modification, all belong to the protection domain of technical solution of the present invention.
Claims (10)
1. the bearing calibration of a medical image metal artifacts, it is characterized in that, comprise: according to segmentation threshold determination metallic region, described segmentation threshold is that the gray-value variation trend in metallic region and other regions in the non-immediate exposure area according to described medical image obtains.
2. the bearing calibration of medical image metal artifacts according to claim 1, it is characterized in that, gray-value variation trend obtains according to the ratio of the pixel number that gray-scale value different in described medical image the is corresponding mean pixel point number corresponding with multiple gray-scale values larger than its gray-scale value.
3. the bearing calibration of medical image metal artifacts according to claim 1, it is characterized in that, gray-value variation trend obtains according to the ratio of the at most corresponding gray-scale value of pixel number in described medical image to the pixel number that the different gray-scale values between maximum gradation value the are corresponding mean pixel point number corresponding with multiple gray-scale values larger than its gray-scale value.
4. the bearing calibration of medical image metal artifacts according to claim 3, is characterized in that, the variation tendency f (i) of gray-scale value obtains according to following formula:
Wherein: histogram (i) is pixel number corresponding when gray-scale value is i, histsum (i+1) is not less than pixel number sum corresponding to the gray-scale value of i+1, i for gray-scale value
maxfor maximum gradation value, the gray-scale value of i representative is not less than pixel number gray-scale value corresponding at most.
5. the bearing calibration of medical image metal artifacts according to claim 4, is characterized in that, in the histogram generated with the corresponding relation between i and f (i), chooses the upper limit of arbitrary gray-scale value corresponding to trough territory as segmentation threshold; Choose the lower limit of arbitrary gray-scale value corresponding to crest territory as segmentation threshold.
6. the bearing calibration of medical image metal artifacts according to claim 4, is characterized in that, in the histogram generated with the corresponding relation between i and f (i), chooses the upper limit of gray-scale value corresponding to minimum trough as segmentation threshold; Choose the lower limit of gray-scale value corresponding to the highest crest as segmentation threshold.
7. a bearing calibration for medical image metal artifacts, is characterized in that, comprising:
The metallic region in described medical image is split according to segmentation threshold;
Front projection is carried out to obtain the data for projection of metallic region to described metallic region;
The data for projection adopting the data for projection of non-metallic regions to upgrade described metallic region generates the data for projection after upgrading;
Carry out reconstruction acquisition first with the data for projection after described renewal and rebuild image;
Rebuild in image described first and recover metallic region to obtain the second reconstruction image, it is characterized in that, the segmentation threshold of described metallic region obtains according to the defining method of any one of claim 1 to 6 segmentation threshold.
8. the bearing calibration of medical image metal artifacts according to claim 7, is characterized in that, carries out metallic region segmentation by clustering procedure or process of iteration to described medical image.
9. the bearing calibration of medical image metal artifacts according to claim 7, it is characterized in that, carry out interpolation to upgrade the data for projection of described metallic region by a kind of data for projection to non-metallic regions in neighbor interpolation, bilinear interpolation, cubic spline interpolation.
10. a means for correcting for medical image metal artifacts, is characterized in that, comprising:
Segmentation threshold determining unit, based on the segmentation threshold of the gray-value variation trend determination metallic region between the metallic region of non-immediate exposure area in described medical image and other regions;
Metallic region cutting unit, splits the metallic region in described medical image according to described segmentation threshold;
Projecting cell, carries out front projection to obtain the data for projection of described metallic region to described metallic region;
Updating block, the data for projection adopting the data for projection of non-metallic regions to upgrade described metallic region generates the data for projection after upgrading;
Reconstruction unit, carries out reconstruction acquisition first with the data for projection after described renewal and rebuilds image;
Acquiring unit, rebuilds in image described first and recovers metallic region to obtain the second reconstruction image.
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