METHOD AND SYSTEM FOR ANATOMY STRUCTURE SEGMENTATION AND MODELING IN AN IMAGE

• US 20100322496A1
• Filed: 02/27/2009
• Published: 12/23/2010
• Est. Priority Date: 02/29/2008
• Status: Abandoned Application

• Current Assignee: Agency For Science Technology and Research (Government of Singapore)
• Original Assignee: Agency For Science Technology and Research (Government of Singapore)
• Inventors: Nowinski, Wieslaw Lucjan, Liu, Jimin, Huang, Su
• Application Number:
  US12/735,962
• Publication Number:
  US 20100322496A1
• Time in Patent Office: Days
• US Class Current: 382/131
• CPC Class Codes: G06T 15/08 Volume rendering
• CPC Class Codes: G06T 19/00 Manipulating 3D models or images for computer graphics
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/20128 Atlas-based segmentation
• CPC Class Codes: G06T 2207/20156 Automatic seed setting
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 2210/41 Medical
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/136 involving thresholding
• CPC Class Codes: G06T 7/187 involving region growing; involving region merging; involving connected component labelling
• CPC Class Codes: G06T 7/33 using feature-based methods

Abstract

A method is proposed for segmenting one or more ventricles in a three-dimensional brain scan image (e.g. MR or CT). The image is registered against a brain model, which ventricle models of each of the one or more ventricles. Respective regions of interest are defined based on the ventricle models. Object regions are first obtained by applying region growing procedure in the regions of interest, and then trimmed based on anatomical knowledge. A 3D surface model of one or more objects is constructed within a 3D space from the segmented structure. A 3D surface is edited and refined by a user selecting amendment points in the 3D space which are indicative of missing detail features. A region of the 3D surface near the selected points is then warped towards the amendment points smoothly, and the modified patch is combined with the rest of the 3D surface yields the accurate anatomy structure model.

Claims

1-19. -19. (canceled)

20. A method for segmenting one or more anatomy structures in a three-dimensional medical image composed of medical data, the method comprising the steps of:

(a) registering a model, which comprises one or more respective anatomy models of each of the one or more anatomy structures, with the image, thereby forming a correspondence between locations in the model and respective locations in the medical image;

(b) according to said correspondence, forming one or more warped models of each of the one or more respective anatomy structures in the image wherein the one or more warped models are fitted to the one or more respective anatomy structures, and defining one or more respective regions of interest in the image based on the one or more warped models, the one or more regions of interest taking the expected shape of the one or more respective anatomy structures;

(c) performing region growing on the one or more regions of interest using the medical data to form respective volumes indicative of the respective anatomy structures; and

(d) segmenting the medical image using the respective volumes.

21. A method according to claim 20, wherein the step (b) further comprises the sub-step of:

defining the one or more respective regions of interest in the image by modifying the size of the one or more warped models.

22. A method according to claim 20, wherein the step (c) comprises, for each of the one or more regions of interest, the sub-steps of:

(i) calculating a respective narrow pair and a wide pair of intensity thresholds;

(ii) defining a kernel region of the region of interest according to the narrow pair of intensity thresholds; and

(iii) expanding the kernel region to include transition regions around the region of interest according to the wide pair of intensity thresholds to form said volume as a connected region.

23. A method according to claim 22 wherein the step (i) comprises the sub-steps of:

(iv) clustering voxels in the region of interest according to their intensities;

(v) calculating a pair of intensity thresholds for each cluster based on the intersection points of each cluster with neighboring clusters;

(vi) defining the pair of intensity thresholds for the cluster containing the intensity of the region of interest as the narrow pair of intensity thresholds; and

(vii) defining a lower limit and an upper limit of a combination of the pairs of intensity thresholds for the cluster containing the intensity of the region of interest and the cluster containing the intensity of the transition regions as the wide pair of intensity thresholds.

24. A method according to claim 22 wherein the step (ii) comprises the sub-steps of:

(viii) binarizing the image with the narrow pair of intensity thresholds to obtain a cluster; and

(ix) extracting a maximum connected region from the cluster according to a 6-neighbor connectivity approach to be the kernel region.

25. A method according to claim 22, wherein the step (iii) comprises the sub-steps of:

(x) determining active boundary voxels of the kernel region, wherein for each active boundary voxel, at least one nearest neighbor of the active boundary voxel lying external to the kernel region has an intensity within the wide pair of intensity thresholds;

(xi) grouping the active boundary voxels into boundary patches according to a 26-neighbor connectivity approach;

(xii) applying region growing on each boundary patch to obtain expanded boundary patches; and

(xiii) expanding the kernel region to include the expanded boundary patches to form a connected region.

26. A method according to claim 25, wherein the at least one nearest neighbor of the active boundary voxel is one of 26 nearest neighbors of the active boundary voxel and the active boundary voxels are grouped into boundary patches such that the active boundary voxels within each boundary patch are 26-neighbor connected.

27. A method according to claim 25, wherein applying region growing on each boundary patch to obtain expanded boundary patches further comprises the sub-steps of:

checking if a stop condition is met and if not, generating a first further patch from the boundary patch;

successively checking if the stop condition is met and if not, generating a new further patch from the most recently generated further patch; and

obtaining the expanded boundary patch for each boundary patch as a sum of the generated further patches.

28. A method according to claim 27, wherein each further patch comprises voxels which are nearest neighbors of the patch from which the further patch is generated and which have intensities within the wide pair of intensity thresholds.

29. A method according to claim 27, wherein the stop condition is met when the further patch to be generated is found to comprise no voxels or when the number of voxels in the further patch to be generated is found to be more than twice the number of voxels in the patch from which the further patch is to be generated.

30. A method according to claim 20, wherein the one or more anatomy structures are ventricles, the medical image composed of medical data is a brain scan image composed of brain scan data and the model is a brain model.

31. A method according to claim 20 in which one of said regions of interest corresponds to a third ventricle in a brain scan image, the method further comprising a trimming step comprising the sub-steps of:

(xiv) projecting the connected region onto a middle sagittal plane to obtain a projected image wherein each pixel in the projected image represents a number of voxels in the connected region along a project line leading to the pixel;

(xv) obtaining a threshold clustering values of the pixels in the projected image into two clusters; and

(xvi) removing voxels from the connected region corresponding to the pixels with values higher than the threshold.

32. A method according to claim 20, in which one of said regions of interest corresponds to a third ventricle in a brain scan image, the brain scan image comprising slices of a brain scan volume, the method further comprising a trimming step comprising the sub-steps of:

(xvii) locating candidate leakage components in the region of interest, wherein each candidate leakage component comprises connected regions lying on respective slices of the brain scan volume;

(xviii) repeatedly determining if a candidate leakage component is a C-shape leakage component; and

(xix) removing candidate leakage components which are determined to be C-shape leakage components;

wherein a candidate leakage component is determined to be a C-shape leakage component if

there is at least one other connected region lying on a neighboring slice of a terminal slice in the brain scan volume, the candidate leakage component not comprising said one other connected region lying on the neighboring slice;

the angle between mass centers of the connected regions comprised within the candidate leakage component for the terminal slice and the neighboring slice of the terminal slice and a mass center of said one other connected region on the neighboring slice of the terminal slice is less than 30°

; and

each connected region comprised within the candidate leakage component is on the superior of remaining parts of the region of interest on each respective slice.

33. A method according to claim 32, wherein the trimming step further comprises the sub-steps of:

(xx) repeatedly determining if a candidate leakage component is a strip-like leakage component; and

(xxi) removing candidate leakage components which are determined to be strip-like leakage components;

wherein a candidate leakage component is determined to be a strip-like leakage component if

the candidate leakage component is located at the posterior of a mass center of the region of interest.

34. A method according to claim 32, wherein the step (xvii) further comprises the sub-steps of:

(xxii) grouping pixels on a first slice of the brain scan volume to form a preliminary candidate leakage component comprising a first connected region; and

(xxiii) repeatedly growing the preliminary candidate leakage component to include a connected region in a next slice until an area ratio of the connected region in the next slice to the connected region in the current slice is greater than a predetermined threshold.

35. A method according to claim 20, in which one of said regions of interest corresponds to a fourth ventricle in a brain scan image, the method further comprising a trimming step comprising the sub-steps of:

(xxiv) identifying a first slice in the image with a maximum number of pixels in the connected region;

(xxv) calculating an increase in the number of pixels in the connected region for subsequent superior axial slices from the first slice;

(xxvi) identifying a leakage slice having the greatest increase in the number of pixels in the connected region; and

(xxvii) removing voxels from the connected region lying superior the leakage slice.

36. A method according to claim 20, in which one of said regions of interest corresponds to a fourth ventricle in a brain scan image, the method further comprising a trimming step comprising the sub-steps of:

(xxviii) identifying a first slice with a greater number of pixels in the connected region as compared to a previous slice; and

(xxix) removing voxels from the connected region lying inferior the first slice.

37. A computer system having a processor arranged to perform a method according to claim 20.

38. A computer program product, readable by a computer and containing instructions operable by a processor of a computer system to cause the processor to perform a method according to claim 20.