CN105913431A - Multi-atlas dividing method for low-resolution medical image - Google Patents

Multi-atlas dividing method for low-resolution medical image Download PDF

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CN105913431A
CN105913431A CN201610224426.4A CN201610224426A CN105913431A CN 105913431 A CN105913431 A CN 105913431A CN 201610224426 A CN201610224426 A CN 201610224426A CN 105913431 A CN105913431 A CN 105913431A
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image
resolution
low
atlas
target image
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祝汉灿
范勇
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University of Shaoxing
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing

Abstract

The invention discloses a multi-atlas dividing method for a low-resolution medical image, wherein the multi-atlas dividing method belongs to the field of image processing technology. The method comprises the steps of setting a low-resolution objective image and N high-resolution atlas images, and assuming a fact that the objective image and the atlas images are linearly registered in a same template space; and then successively performing a dividing object area cutting step; an objective image super-resolution recovering step; an image registering and label propagation step; and a label fusion step. According to the multi-atlas dividing method for the low-resolution medical image, an image super-resolution restoring method is merged into a multi-atlas dividing frame; and through improving registering precision between the high-resolution atlas images and the low-resolution to-be-divided image, dividing precision of the multi-atlas dividing method.

Description

The multichannel chromatogram dividing method of low resolution medical image
Technical field
The present invention relates to the multichannel chromatogram dividing method of a kind of low resolution medical image, belong to technical field of image processing.
Background technology
Along with the development of medical imaging devices and universal, medical image analysis is in sides such as disease research, surgery planning, clinical diagnosises Face plays particularly important effect.Medical image segmentation is an important step of medical image analysis, its essence is and treats point The each pixel or the voxel that cut image carry out label, and the pixel of different attribute or voxel set different label values, thus will Image to be split is divided into non-overlapping interest region and background area, in order to follow-up interest region is further analyzed and Process.The generally segmentation to medical image is completed by manually marking interest region, and the advantage of the method is segmentation result accuracy High.But, artificial dividing method takes time and effort very much, and segmentation result is the most not reproducible.In order to reach automatic and accurate The purpose of Medical Image Segmentation, many classical image partition methods have been widely used in the segmentation of all kinds of medical image.
Dividing method based on collection of illustrative plates, the collection of illustrative plates owing to being used not only contains the half-tone information of image, and provides image The prior informations such as the shape of middle zones of different, therefore can obtain more accurate segmentation result compared to other dividing method.Root According to the quantity of use collection of illustrative plates, dividing method based on collection of illustrative plates can be divided into free hand drawing spectrum dividing method and multichannel chromatogram dividing method.Free hand drawing Spectrum dividing method is owing to only with single collection of illustrative plates, just having obtained mesh after collection of illustrative plates label image being traveled to target image by registration The segmentation result of logo image.And multichannel chromatogram dividing method uses multiple collection of illustrative plates, by registration, each collection of illustrative plates label image is traveled to Having obtained multiple segmentations of target image after target image, these segmentation results are combined by tag fusion and obtain final dividing Cut result.Therefore, multichannel chromatogram dividing method can be largely classified into image registration and two steps of tag fusion.With free hand drawing spectrum point Segmentation method is compared, and multichannel chromatogram dividing method, owing to employing more collection of illustrative plates, therefore contains the individual difference letter in region to be split Breath, and can also effectively eliminate, by tag fusion, the error that in free hand drawing spectral method, gray level image registration introduces.
Multichannel chromatogram dividing method obtains in the segmentation field of medical image at present and applies widely.But according to the report of current document Lead, it has been found that: atlas image that (1) multichannel chromatogram dividing method uses and target image often from same image set, I.e. there is identical picture quality (resolution ratio).(2) multichannel chromatogram dividing method is on different data sets, to identical brain structure Splitting, the segmentation result obtained differs huge in accuracy.Such as the segmentation of hippocampus, current main flow Multichannel chromatogram method can obtain segmentation precision at high-resolution MRI data set (such as ADNI data set) and (use Dice Value metric, Dice value the biggest expression precision is the highest) it is 0.9;And at low resolution image data collection (such as mTLE data set), The segmentation precision obtained is only 0.72.
In the actual application of multichannel chromatogram dividing method, owing to collection of illustrative plates constructs in advance, full resolution pricture is generally used to be configured to To high-quality collection of illustrative plates;But being affected by the factor such as image acquisition time, collecting device, the quality of target image cannot ensure, The target image of low resolution can be obtained in the case of a lot.Therefore, how to split low resolution target image with high-resolution collection of illustrative plates, The actual application of multichannel chromatogram dividing method has important researching value.
In view of this, this is studied by the present inventor, develops the multichannel chromatogram segmentation side of a kind of low resolution medical image specially Method, this case thus produces.
Summary of the invention
It is an object of the invention to provide the multichannel chromatogram dividing method of a kind of low resolution medical image, in conjunction with superresolution restoration method Improve dividing method based on multichannel chromatogram, propose accurate, the low resolution medical science brain image segmentation method of robust.
To achieve these goals, the solution of the present invention is:
The multichannel chromatogram dividing method of low resolution medical image, comprises the steps:
Step one, given low resolution target image Id, N number of high-resolution atlas image Ai=(Ii,Li), i=1,2 ..., N, its Middle IiRepresent i-th gray level image, LiRepresent the label image that i-th gray level image is corresponding, and suppose target image IdAnd Atlas image Ai=(Ii,Li), i=1,2 ..., N, the most linearly it is registrated to same templatespace;
Step 2, the cutting of cutting object region: scan all atlas image Ai=(Ii,Li), i=1,2 ..., N, in label image, (x, y, z) position, if extending along on three coordinate directions to find out the minimum and maximum three-dimensional coordinate of cutting object in each collection of illustrative plates Dry voxel is so that the cutting object in target image can be included;
Step 3, target image superresolution restoration: use image super-resolution restoration methods based on rarefaction representation to target figure As IdCarry out superresolution restoration, by low resolution target image IdRevert to the higher resolution image I of identical contenth
Step 4, image registration and label are propagated: to each atlas image Ai=(Ii,Li), i=1,2 ..., N., independently it And superresolution restoration after target image IhRegistrate, by registration, obtain each atlas image Ai=(Ii,Li), i=1,2 ..., N. to target image IdDeformation Field, utilize above-mentioned Deformation Field by atlas image Ai=(Ii,Li), i=1,2 ..., the label image of N. travels to target image space, then obtains each atlas image to target image Segmentation result;
Step 5, tag fusion: use most of voting method to carry out tag fusion, for each voxel of target image, Its label value is determined by that label value that each atlas image correspondence position occurrence number is most.
As preferably: image super-resolution restoration methods described in step 3 particularly as follows:
Utilize one group of high-definition picture { Ii, i=1,2 ..., N} as training set by low resolution target image IdRevert to identical interior The higher resolution image I heldh
High-definition picture IhTo low-resolution image IdThe process that degrades typically represent by following Mathematical Modeling
Id=LMBIh, (1)
Here B and LMRepresent respectively image IhCarry out fuzzy and down-sampling;
Image super-resolution method constructs high resolution graphics image set { I first with the model that degrades (1)i, i=1,2 ..., corresponding low of N} Resolution chart image set, is designated asThen, high-definition picture block dictionary D is constructed according to the two image seth And the low-resolution image block dictionary D of correspondenced, for low-resolution image IdEach image block pd, utilize sparse table Representation model
m i n α | | D d α - p d | | 2 2 + λ | | α | | 1 , - - - ( 2 )
Low-resolution image block p can be obtaineddIn low-resolution dictionary DdIn expression: pd=Ddα, it is assumed that corresponding high-resolution Image block phAt high-definition picture block dictionary DhIn there is identical representation (i.e. having identical expression factor alpha), then High-definition picture block p can be calculatedh=Dhα, by low-resolution image IdAll image blocks recover correspondence high-resolution After rate image block, initial full resolution pricture I can be obtained through assembly0, by an overall regularization model
I h = argmin I | | I - I 0 | | , s . t . L M B I = I d , - - - ( 3 )
Obtain final full resolution pricture Ih
As preferably, the registration described in step 4 is to be obtained by the nonlinear algorithm driven based on crosscorrelation, available public generation Code kit ANTs realizes.
The multichannel chromatogram dividing method of low resolution medical image of the present invention, is dissolved into many figures by Image Super-resolution restoration methods Among spectrum segmentation framework, by improving the registration accuracy between high-resolution atlas image and low resolution image to be split, thus carry The segmentation precision of high multichannel chromatogram dividing method.Improve multichannel chromatogram dividing method the highest to low resolution target image segmentation accuracy Situation.
Below in conjunction with drawings and the specific embodiments, the present invention is described in further detail.
Accompanying drawing explanation
Fig. 1 is the multichannel chromatogram dividing method flow chart of the low resolution medical science brain image of the present embodiment;
Fig. 2 is one of them tested MRI of the present embodiment, hippocampus segmentation image and the low-resolution image of structure (first and second images are a tested magnetic resonance gray level image from ADNI database and the segmentation of corresponding hippocampus Figure, the 3rd image is the low-resolution image constructed by obfuscation and down-sampling);
Fig. 3 is the segmentation result example of the present embodiment, and first row is former 3.0T image and corresponding artificial segmentation (as goldstandard); Its segmentation is obtained by secondary series is obfuscation and down-sampling obtains low resolution target image and original multichannel chromatogram dividing method Segmentation result;3rd row be superresolution restoration after target image and the present embodiment the segmentation result that obtains of method is proposed.
Detailed description of the invention
The multichannel chromatogram dividing method of low resolution medical image, comprises the steps:
Step one, given low resolution target image Id, N number of high-resolution atlas image Ai=(Ii,Li), i=1,2 ..., N, its Middle IiRepresent i-th gray level image, LiRepresent the label image that i-th gray level image is corresponding, and suppose target image IdAnd Atlas image Ai=(Ii,Li), i=1,2 ..., N, the most linearly it is registrated to same templatespace;
Step 2, the cutting of cutting object region:
As it is shown in figure 1, for reducing amount of calculation, first the region comprising cutting object is cut down.Due to target Image IdAnd atlas image Ai=(Ii,Li), i=1,2 ..., N, the most linearly it is registrated to a templatespace, has therefore been split Object position in each image is the most similar, scans all atlas image Ai=(Ii,Li), i=1,2 ..., N, in label image (i.e. Scan all segmentation results), (x, y, z) position, along three to find out the minimum and maximum three-dimensional coordinate of cutting object in each collection of illustrative plates Several voxels are extended so that the cutting object in target image can be included on individual coordinate direction.For description side Just, target image and atlas image after cutting are still designated as IdAnd Ai=(Ii,Li), i=1,2 ..., N.
Step 3, target image superresolution restoration:
Use image super-resolution restoration methods based on rarefaction representation to target image IdCarry out superresolution restoration: utilize one group High-definition picture { Ii, i=1,2 ..., N} as training set by low resolution target image IdRevert to the more high-resolution of identical content Rate image Ih
High-definition picture IhTo low-resolution image IdThe process that degrades typically represent by following Mathematical Modeling
Id=LMBIh, (1)
Here B and LMRepresent respectively image IhCarry out fuzzy and down-sampling.
Image super-resolution restoration methods constructs high resolution graphics image set { I first with the model that degrades (1)i, i=1,2 ..., N} is corresponding Low-resolution image collection, be designated asThen, high-definition picture block is constructed according to the two image set Dictionary DhAnd the low-resolution image block dictionary D of correspondenced.For low-resolution image IdEach image block pd, utilize Sparse representation model
m i n α | | D d α - p d | | 2 2 + λ | | α | | 1 , - - - ( 2 )
Low-resolution image block p can be obtaineddIn low-resolution dictionary DdIn expression: pd=Ddα.Assuming that corresponding high score Resolution image block phAt high-definition picture block dictionary DhIn there is identical representation (i.e. having identical expression factor alpha), Then high-definition picture block p can be calculatedh=Dhα.By low-resolution image IdAll image blocks recover correspondence height After image in different resolution block, initial full resolution pricture I can be obtained through assembly0, by an overall regularization model
I h = argmin I | | I - I 0 | | , s . t . L M B I = I d , - - - ( 3 )
Obtain final full resolution pricture Ih
Step 4, image registration and label are propagated:
To each atlas image Ai=(Ii,Li), i=1,2 ..., N., independently the target figure after they and superresolution restoration As IhRegistrating, registration is to be obtained by the nonlinear algorithm driven based on crosscorrelation, available common code kit ANTs Realize.By registration, obtain each atlas image Ai=(Ii,Li), i=1,2 ..., N. to target image IdDeformation Field.Profit By above-mentioned Deformation Field by atlas image Ai=(Ii,Li), i=1,2 ..., the label image of N. travels to target image space, then obtains Each atlas image segmentation result to target image.
Step 5, tag fusion:
Using most of voting method to carry out tag fusion, for each voxel of target image, its label value is by each collection of illustrative plates That label value decision that image correspondence position occurrence number is most.
The multichannel chromatogram dividing method of the low resolution medical image described in the present embodiment, is dissolved into many by Image Super-resolution restoration methods Among collection of illustrative plates segmentation framework, by improving the registration accuracy between high-resolution atlas image and low resolution image to be split, thus Improve the segmentation precision of multichannel chromatogram dividing method.Improve multichannel chromatogram dividing method to low resolution target image segmentation accuracy not High situation.
In the present embodiment, use from the 30 of Alzheimer disease image center database (ADNI, adni.loni.ucla.edu) Individual tested 3.0T MRI verifies the multichannel chromatogram dividing method of above-mentioned low resolution medical image, and cutting object is brain Hippocampus.For each tested, the method for manual segmentation is first used to obtain the hippocampus segmentation of correspondence;By image blurringization and Down-sampling, the low-resolution image that structure is corresponding.Fig. 2 shows one of them tested MRI, hippocampus segmentation figure Picture and the low-resolution image of structure, for convenience of showing, have chosen one of them two dimension tomography and be indicated.In these data In, the low resolution image of structure, is adopted as target image to be split, original 3.0T MRI as training data The method verifying proposition by the mode staying a checking.
Parameter sets:
Dictionary size in image super-resolution restoration methods, random in each training data chooses 100 image blocks, word Allusion quotation size is 100*29 that is 2900.The dictionary structure of low-resolution image, when extracting feature with principal component analysis (PCA), We set the ratio of keeping characteristics dimension as 10%, and the size of the image block extracted in low resolution is set as 3 × 3 × 3.High score The scale factor of resolution image and low-resolution image is 2 × 2 × 2 for ADNI data setting.
Segmentation result:
Calculate segmentation precision by Dice value, be defined as follows, given artificial segmentation result E (as goldstandard) and from Dynamic segmentation result F,
D i c e = 2 V ( E ∩ F ) V ( E ) + V ( F ) ,
Here V (X) represents the volume of segmentation result X.
Multichannel chromatogram dividing method based on superresolution restoration described in the present embodiment and original multichannel chromatogram dividing method are compared Relatively.
The segmentation that multichannel chromatogram dividing method described in the segmentation result of the multichannel chromatogram dividing method that table 1. is original and the present embodiment obtains The Dice mean value of result and standard deviation.
Table 1 lists Dice mean value and the standard deviation of segmentation result, and compares original multichannel chromatogram with the inspection of double sample t The otherness of the segmentation result that the multichannel chromatogram dividing method described in the segmentation result of dividing method and the present embodiment obtains.Experimental result Display, in level of significance α value 10-5In the case of, the multichannel chromatogram dividing method described in the present embodiment to be significantly better than original Multichannel chromatogram dividing method.
Fig. 3 illustrates a tested segmentation situation at random, and first row is former 3.0T image and corresponding manually splitting (as gold Standard);It is split by secondary series is obfuscation and down-sampling obtains low resolution target image and original multichannel chromatogram dividing method The segmentation result obtained;3rd row be superresolution restoration after target image and the present embodiment propose method obtain segmentation knot Really.It can be seen that: (1) uses superresolution restoration method to improve quality (2,3 two of contrast the first row of image Figure);(2) combine superresolution restoration method and segmentation result that multichannel chromatogram dividing method obtains is more accurately (by the second row 2,3 Two figures contrast 1 figure manually split can be seen that).
Conclusion: by combining superresolution restoration method, low resolution target image is carried out superresolution restoration, can actually carry The accuracy of high multichannel chromatogram dividing method and robustness.
Above-described embodiment and the product form of the graphic and non-limiting present invention and style, the ordinary skill people of any art Member is suitably changed what it did or modifies, and all should be regarded as the patent category without departing from the present invention.

Claims (3)

1. the multichannel chromatogram dividing method of low resolution medical image, it is characterised in that comprise the steps:
Step one, given low resolution target image Id, N number of high-resolution atlas image Ai=(Ii,Li), i=1,2 ..., N, wherein IiTable Show i-th gray level image, LiRepresent the label image that i-th gray level image is corresponding, and suppose target image IdAnd figure spectrogram As Ai=(Ii,Li), i=1,2 ..., N, the most linearly it is registrated to same templatespace;
Step 2, the cutting of cutting object region: scan all atlas image Ai=(Ii,Li), i=1,2 ..., N, in label image, find out In each collection of illustrative plates, (x, y, z) position extend several on three coordinate directions to the minimum and maximum three-dimensional coordinate of cutting object Voxel is so that the cutting object in target image can be included;
Step 3, target image superresolution restoration: use image super-resolution restoration methods based on rarefaction representation to target image IdEnter Row superresolution restoration, by low resolution target image IdRevert to the higher resolution image I of identical contenth
Step 4, image registration and label are propagated: to each atlas image Ai=(Ii,Li), i=1,2 ..., N., independently they and Target image I after superresolution restorationhRegistrate, by registration, obtain each atlas image Ai=(Ii,Li), i=1,2 ..., N. to target image IdDeformation Field, utilize above-mentioned Deformation Field by atlas image Ai=(Ii,Li), i=1,2 ..., the label image of N. travels to target image space, then obtains each atlas image to target image Segmentation result;
Step 5, tag fusion: use most of voting method to carry out tag fusion, for each voxel of target image, its Label value is determined by that label value that each atlas image correspondence position occurrence number is most.
2. the multichannel chromatogram dividing method of low resolution medical image as claimed in claim 1, it is characterised in that: described in step 3 Image super-resolution restoration methods particularly as follows:
Utilize one group of high-definition picture { Ii, i=1,2 ..., N} as training set by low resolution target image IdRevert to identical content Higher resolution image Ih
High-definition picture IhTo low-resolution image IdThe process that degrades typically represent by following Mathematical Modeling
Id=LMBIh, (1)
Here B and LMRepresent respectively image IhCarry out fuzzy and down-sampling;
Image super-resolution restoration methods constructs high resolution graphics image set { I first with the model that degrades (1)i, i=1,2 ..., corresponding low of N} Resolution chart image set, is designated asThen, high-definition picture block dictionary D is constructed according to the two image seth And the low-resolution image block dictionary D of correspondenced, for low-resolution image IdEach image block pd, utilize sparse table Representation model
m i n α | | D d α - p d | | 2 2 + λ | | α | | 1 , - - - ( 2 )
Low-resolution image block p can be obtaineddIn low-resolution dictionary DdIn expression: pd=Ddα, it is assumed that corresponding high-resolution Image block phAt high-definition picture block dictionary DhIn there is identical representation, then can calculate high-definition picture block ph=Dhα, by low-resolution image IdAll image blocks recover correspondence high-definition picture block after, can through assembly To obtain initial full resolution pricture I0, by an overall regularization model
I h = argmin I | | I - I 0 | | , s . t . L M B I = I d , - - - ( 3 )
Obtain final full resolution pricture Ih
3. the multichannel chromatogram dividing method of low resolution medical image as claimed in claim 1, it is characterised in that: described in step 4 Registration is to be obtained by the nonlinear algorithm driven based on crosscorrelation.
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CN113935928A (en) * 2020-07-13 2022-01-14 四川大学 Rock core image super-resolution reconstruction based on Raw format
CN113935928B (en) * 2020-07-13 2023-04-11 四川大学 Rock core image super-resolution reconstruction based on Raw format
CN116228786A (en) * 2023-05-10 2023-06-06 青岛市中心医院 Prostate MRI image enhancement segmentation method, device, electronic equipment and storage medium
CN116228786B (en) * 2023-05-10 2023-08-08 青岛市中心医院 Prostate MRI image enhancement segmentation method, device, electronic equipment and storage medium

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Application publication date: 20160831