MEDICAL IMAGE ANALYSIS SYSTEM FOR DISPLAYING ANATOMICAL IMAGES SUBJECT TO DEFORMATION AND RELATED METHODS

• US 20110081054A1
• Filed: 10/02/2009
• Published: 04/07/2011
• Est. Priority Date: 10/02/2009
• Status: Abandoned Application

• Current Assignee: Harris Corporation (L3Harris Technologies, Inc.)
• Original Assignee: Harris Corporation (L3Harris Technologies, Inc.)
• Inventors: Jackson, Jeremy D., Burrell, Lauren S., Culp, Timothy R., Bell, David M.
• Application Number:
  US12/572,596
• Publication Number:
  US 20110081054A1
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/20076 Probabilistic image processing
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 7/35 using statistical methods
• CPC Class Codes: G06T 7/38 Registration of image sequences

Abstract

A medical image analysis system is for first and second anatomical image data of a same body area and subject to deformation. The medical image analysis system may include a processor cooperating with a memory to generate a deformation vector array between the first and second anatomical image data. The processor may also display, on a display, first and second anatomical images respectively based upon the first and second anatomical image data. A first cursor may be displayed on the first anatomical image, and a second cursor may be displayed on the second anatomical image based upon a mapping of the first cursor using the deformation vector array.

Claims

1. A medical image analysis system for first and second anatomical image data of a same body area and subject to deformation, the medical image analysis system comprising:

a memory; and

a processor cooperating with said memory and configured to

generate a deformation vector array between the first and second anatomical image data,

display, on a display, first and second anatomical images respectively based upon the first and second anatomical image data,

display a first cursor on the first anatomical image, and

display a second cursor on the second anatomical image based upon a mapping of the first cursor using the deformation vector array.

2. The medical image analysis system of claim 1 wherein the first and second anatomical image data each comprise respective first and second pluralities of two-dimensional anatomical image slice data.

3. The medical image analysis system of claim 2 wherein said processor displays the first and second anatomical images based upon respective ones of the first and second pluralities of two-dimensional anatomical image slice data.

4. The medical image analysis system of claim 1 wherein the first and second anatomical image data have different resolutions;

and wherein said processor is further configured to resample at least one of the first and second anatomical image data to a common resolution.

5. The medical image analysis system of claim 1 wherein the first and second anatomical image data comprise respective first and second sets of voxels;

and wherein said processor is configured to generate the deformation vector array by at least;

generating a respective reach array for each voxel of the second anatomical image data, each reach array comprising a subset of contiguous voxels;

generating a cost array for each reach array, each cost array based upon probabilities of voxels of the reach array matching voxels of the first anatomical image data; and

solving each cost array using belief propagation to thereby generate the deformation vector array between the first and second anatomical image data.

6. The medical image analysis system of claim 5 wherein an initial reach array is a fixed reach array and each subsequent reach array is a variable reach array.

7. The medical image analysis system of claim 5 wherein each reach array is a three-dimensional array of three-dimensional array descriptors.

8. The medical image analysis system of claim 5 wherein each cost array is a three-dimensional array of three-dimensional sub-arrays of scalar cost values.

9. The medical image analysis system of claim 5 said processor is further configured to solve each cost array by at least:

generating N belief messages for each cost message, each belief message being based upon another cost message and N−

1 belief messages associated therewith;

adding each cost message with the N belief messages associated therewith to generate a cost-belief sum; and

forming a vector of the deformation array based upon a smallest cost-belief sum of each cost array.

12. The medical image analysis system of claim 1 wherein the first and second anatomical image data comprise respective first and second sets of voxels;

and wherein said processor is configured to generate the deformation vector array by at least;

generating a respective reach array for each voxel of the second anatomical image data, each reach array comprising a subset of contiguous voxels;

generating a cost array for each reach array, each cost array based upon probabilities of voxels of the reach array matching voxels of the first anatomical image data; and

solving each cost array using belief propagation to thereby generate the deformation vector array between the first and second anatomical image data.

10. A medical image analysis system for first and second anatomical image data of a same body area and subject to deformation, the first and second anatomical image data each comprising respective first and second pluralities of two-dimensional anatomical image slice data and having different resolutions, the medical image analysis system comprising:

a memory; and

a processor cooperating with said memory and configured to

resample at least one of the first and second anatomical image data to a common resolution,

generate a deformation vector array between the first and second anatomical image data,

display, on a display, first and second anatomical images respectively based upon the first and second anatomical image data,

display a first cursor on the first anatomical image, and

display a second cursor on the second anatomical image based upon a mapping of the first cursor using the deformation vector array.

11. The medical image analysis system of claim 10 wherein said processor displays the first and second anatomical images based upon respective ones of the first and second pluralities of two-dimensional anatomical image slice data.

13. An image analysis system for first and second image data of a same area and subject to deformation, the image analysis system comprising:

a memory; and

a processor cooperating with said memory and configured to

generate a deformation vector array between the first and second image data,

display, on a display, first and second images respectively based upon the first and second anatomical image data,

display a first cursor on the first anatomical image, and

display a second cursor on the second image based upon a mapping of the first cursor using the deformation vector array.

14. The image analysis system of claim 13 wherein the first and second image data each comprises respective first and second pluralities of two-dimensional image slice data;

and wherein said processor displays the first and second images based upon respective ones of the first and second pluralities of two-dimensional image slice data.

15. The image analysis system of claim 13 wherein the first and second image data have different resolutions;

and wherein said processor is further configured to resample at least one of the first and second image data to a common resolution.

16. A method of operating a medical image analysis system for first and second anatomical image data of a same body area and subject to deformation, the method comprising:

generating, using a processor, a deformation vector array between the first and second anatomical image data;

displaying, on a display, first and second anatomical images respectively based upon the first and second anatomical image data;

displaying, on the display, a first cursor on the first anatomical image; and

displaying, on the display, a second cursor on the second anatomical image based upon a mapping of the first cursor using the deformation vector array.

17. The method of claim 16 wherein the first and second anatomical image data each comprises respective first and second pluralities of two-dimensional anatomical image slice data.

18. The method of claim 17 wherein the first and second anatomical images are displayed based upon respective ones of the first and second pluralities of two-dimensional anatomical image slice data.

19. The method of claim 16 wherein the first and second anatomical image data have different resolutions;

and further comprising resampling, using the processor, at least one of the first and second anatomical image data to a common resolution.

20. The method of claim 16 wherein the first and second anatomical image data comprise respective first and second sets of voxels;

and wherein the deformation vector array is generated, using the processor, by at least;

generating a respective reach array for each voxel of the second anatomical image data, each reach array comprising a subset of contiguous voxels;

generating a cost array for each reach array, each cost array based upon probabilities of voxels of the reach array matching voxels of the first anatomical image data; and

solving each cost array using belief propagation to thereby generate the deformation vector array between the first and second anatomical image data.

21. The method of claim 20 wherein an initial reach array is a fixed reach array and each subsequent reach array is a variable reach array.

22. The method of claim 20 wherein each reach array is a three-dimensional array of three-dimensional array descriptors.

23. The method of claim 20 wherein each cost array is a three-dimensional array of three-dimensional sub-arrays of scalar cost values.

24. The method of claim 20 wherein each cost array is solved by at least:

generating N belief messages for each cost message, each belief message being based upon another cost message and N−

1 belief messages associated therewith;

adding each cost message with the N belief messages associated therewith to generate a cost-belief sum; and

forming a vector of the deformation array based upon a smallest cost-belief sum of each cost array.