Method, system and computer readable medium for computerized processing of contralateral and temporal subtraction images using elastic matching

• US 6,594,378 B1
• Filed: 10/20/2000
• Issued: 07/15/2003
• Est. Priority Date: 10/21/1999
• Status: Expired due to Term

• Current Assignee: ARCH Development Corporation (University of Chicago)
• Original Assignee: ARCH Development Corporation (University of Chicago)
• Inventors: Katsuragawa, Shigehiko, Li, Qiang, Doi, Kunio
• Primary Examiner(s): Boudreau, Leo
• Assistant Examiner(s): Choobin, Barry
• Application Number:
  US09/692,218
• Time in Patent Office: 998 Days
• Field of Search: 382/128, 382/132, 382/130, 382/100, 382/108, 382/199, 382/151, 128/922, 356/39, 377/10
• US Class Current: 382/128
• CPC Class Codes: G06T 2207/20061 Hough transform
• CPC Class Codes: G06T 2207/30008 Bone
• CPC Class Codes: G06T 2207/30061 Lung
• CPC Class Codes: G06T 3/14 Transformations for image registration, e.g. adjusting or mapping for alignment of images
• CPC Class Codes: G06T 5/50 using two or more images, e.g. averaging or subtraction
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06T 7/149 involving deformable models, e.g. active contour models
• CPC Class Codes: G06T 7/174 involving the use of two or more images
• CPC Class Codes: G06T 7/344 involving models
• CPC Class Codes: Y10S 128/922 including image analysis

Abstract

A method, system and computer readable medium of computerized processing of chest images including obtaining digital first and second images of a chest and detecting rib edges in at least one of the first and second images. The rib edges are detected by correlating points in the at least one of the first and second images to plural rib edge models using a Hough transform to identify approximate rib edges in one of the images, and delineating actual rib edges derived from the identified approximate rib edges using a snake model. The method system and computer readable medium further include deriving the shift values using the actual rib edges and warping one of the first and second images to produce a warped image which is registered to the other of the first and second images based at least in part on the shift values.

Claims

1. A method of computerized processing of chest images, comprising:

obtaining digital first and second images of a chest;

detecting internal rib edges of ribs located interior of a rib cage and overlaying a lung in at least one of said first and second images, comprising,

correlating points in said at least one of said first and second images to plural rib edge models using a generalized Hough transform to identify approximate internal rib edges in said one of said images, and

delineating actual internal rib edges derived from the identified approximate internal rib edges using a snake model;

deriving shift values using said actual internal rib edges; and

warping one of said first and second images to produce a warped image which is registered to an other of said first and second images based at least in part on said shift values.

2. The method of claim 1, further comprising subtracting said warped image from said other image to generate a subtraction image.

3. The method of claim 2, further comprising displaying the subtraction image.

4. The method of claim 1, wherein said step of deriving shift values comprises deriving global shift values;

and

said step of warping comprises producing a globally warped image which is registered to said other of said first and second images based at least in part on said global shift values.

5. The method of claim 4, wherein said step of warping comprises:

deriving local shift values from a difference between said globally warped image and a non-warped image; and

locally warping said globally warped image to produce a locally warped image which is registered to said non-warped image using said local shift values.

6. The method of claim 4, wherein said step of deriving global shift values comprises:

determining correspondence of rib edges between said first and second images;

determining vertical shift values for points on rib edges in one of said first and second images; and

fitting said vertical shift values to a two dimensional polynomial.

7. The method of claim 5, wherein said step of deriving local shift values comprises:

selecting template regions of interest (ROIs) in one of said globally warped image and said non-warped image and search area ROIs in the other of said globally warped image and said non-warped image;

determining cross correlation values between pixels in said template ROIs and said search area ROIs;

determining local shift vectors for said template ROIs, comprising,

determining local shift vectors between pixels in said search ROIs relative to said template ROIs,

determining internal and external energies based on smoothness of said local shift vectors and said cross correlation values, respectively, and

modifying said local shift vectors based at least in part on said internal and external energies; and

producing said local shift values based on said modified local shift vectors.

8. The method of claim 7, wherein said step of deriving local shift values further comprises performing bilinear interpolation on said modified local shift vectors on image pixels of two lungs of one of said first and second images.

9. The method of claim 1, further comprising performing edge enhancement on said at least one of said first and second images, prior to performing said step of correlating points.

10. The method of claim 1, wherein said step of obtaining digital first and second images comprises:

obtaining said digital first image; and

obtaining as said second image a mirror image of said digital first image.

11. The method of claim 10, further comprising performing lateral inclination correction on said digital first image prior to obtaining said mirror image.

12. The method of claim 1, wherein said step of obtaining digital first and second images comprises obtaining temporally separated digital first and second images of a same chest.

13. The method of claim 12, further comprising performing lateral inclination correction on said temporally separated digital first and second images.

14. The method of claim 12, wherein said step of detecting rib edges comprises detecting actual rib edges in both of said temporally separated digital first and second images.

15. The method of claim 13, wherein said step of detecting rib edges comprises detecting actual rib edges in both of said temporally separated digital first and second images.

16. The method of claim 7, wherein said step of obtaining digital first and second images comprises:

obtaining said digital first image; and

obtaining as said second image a mirror image of said digital first image.

17. The method of claim 16, further comprising performing lateral inclination correction on said digital first image prior to obtaining said mirror image.

18. The method of claim 7, wherein said step of obtaining digital first and second images comprises obtaining temporally separated digital first and second images of a same chest.

19. The method of claim 18, further comprising performing lateral inclination correction on said temporally separated digital first and second images.

20. The method of claim 18, wherein said step of detecting rib edges comprises detecting actual rib edges in both of said temporally separated digital first and second images.

21. The method of claim 20, wherein said step of detecting rib edges comprises detecting actual rib edges in both of said temporally separated digital first and second images.

31. An image processing system configured to perform the steps recited in one of claims 1 to 30.

32. A storage medium storing a program for performing the steps recited in one of claims 1 to 30.

41. The method according to claim 1, wherein the correlating step comprises:

detecting lower edges of posterior ribs.

42. The method according to claim 41, wherein the correlating step further comprises selecting as edge pixels pixels having edge gradient orientations between 90°

and 170°

.

43. The method according to claim 41, wherein the correlating step further comprises identifying pixels at lower edges of posterior ribs.

44. A computer program product storing program instructions, which when executed by a computer system, cause the computer system to perform the method recited in any one of claims 41-43.

45. A system implementing any one of claims 41-43.

22. A method of computerized processing of chest images, comprising:

obtaining digital first and second images of a chest including internal rib edges of ribs located interior of a rib cage and overlaying a lung;

selecting template regions of interest (ROIs) in one of said first and second images and search area ROIs in an other of said first and second images;

determining cross correlation values between pixels in said template ROIs and said search area ROIs;

determining shift vectors for said template ROIs, comprising,

determining local shift vectors between pixels in said search ROIs relative to said template ROIs,

determining internal and external energies based on smoothness of said local shift vectors and said cross correlation values, respectively, and

modifying said local shift vectors based at least in part on said internal and external energies;

producing said shift vectors based on said modified local shift vectors; and

warping one of said first and second images to produce a warped image which is registered to said other of said first and second images based at least in part on said shift vectors.

23. The method of claim 22, further comprising subtracting said warped image from said other image to generate a subtraction image.

24. The method of claim 22, further comprising displaying said subtraction image.

25. The method of claim 22, wherein said step of producing shift vectors further comprises performing bilinear interpolation on said modified local shift vectors on image pixels of two lungs of one of said first and second images.

26. The method of claim 22, wherein said step of obtaining said first and second images comprises performing preprocessing for density correction, lateral inclination correction and global matching on at least one of said first and second images.

27. The method of claim 22, wherein said step of obtaining digital first and second images comprises:

obtaining said digital first image; and

obtaining as said second image a mirror image of said digital first image.

28. The method of claim 27, further comprising performing lateral inclination correction on said digital first image prior to obtaining said mirror image.

29. The method of claim 22, wherein said step of obtaining digital first and second images comprises obtaining temporally separated digital first and second images of a same chest.

30. The method of claim 29, further comprising performing lateral inclination correction on said temporally separated digital first and second images.

33. A method of computerized processing of chest images, comprising:

obtaining digital first and second images of a chest;

detecting internal rib edges of ribs located interior of a rib cage and overlaying a lung in at least one of said first and second images, comprising;

correlating points in said at least one of said first and second images to plural rib edge models using a Hough transform to identify approximate rib edges in said one of said images, and

delineating actual rib edges derived from the identified approximate rib edges using a snake model;

deriving shift values using said actual rib edges; and

warping one of said first and second images to produce a warped image which is registered to an other of said first and second images based at least in part on said shift values;

wherein said step of obtaining digital first and second images comprises;

obtaining said digital first image; and

obtaining as said second image a mirror image of said digital first image.

34. The method of claim 33, further comprising performing lateral inclination correction on said digital first image prior to obtaining said mirror image.

39. The method of one of claim 33 or 35, wherein the detecting the rib edges step comprises detecting internal rib edges of ribs located interior of a rib cage.

40. The method of one of claim 33 or 35, wherein the correlating points step comprises correlating points in said at least one of said first and second images to plural rib edge models using a generalized Hough transform to identify approximate rib edges in said one of said images.

35. A method of computerized processing of chest images, comprising:

obtaining digital first and second images of a chest;

detecting internal rib edges of ribs located interior of a rib cage and overlaying a lung in at least one of said first and second images, comprising;

 

correlating points in said at least one of said first and second images to plural rib edge models using a Hough transform to identify approximate rib edges in said one of said images, and

 

delineating actual rib edges derived from the identified approximate rib edges using a snake model;

 

deriving shift values using said actual rib edges; and

 

warping one of said first and second images to produce a warped image which is registered to an other of said first and second images based at least in part on said shift values; and

 

wherein said step of deriving shift values comprises deriving global shift values; and

 

said step of warping comprises producing a globally warped image which is registered to said other of said first and second images based at least in part on said global shift values; and

 

wherein said step of warping comprises,

deriving local shift values from a difference between said globally warped image and a non-warped image, and

locally warping said globally warped image to produce a locally warped image which is registered to said non-warped image using said local shift values; and

wherein said step of deriving local shift values comprises,

 

selecting template regions of interest (ROIs) in one of said globally warped image and said non-warped image and search area ROIs in an other of said globally warped image and said non-warped image,

 

determining cross correlation values between pixels in said template ROIs and said search area ROIs,

 

determining local shift vectors for said template ROIs, comprising,

determining local shift vectors between pixels in said search ROIs relative to said template ROIs,

determining internal and external energies based on smoothness of said local shift vectors and said cross correlation values, respectively, and

modifying said local shift vectors based at least in part on said internal and external energies; and

producing said local shift values based on said modified local shift vectors; and

wherein said step of obtaining digital first and second images comprises,

 

obtaining s aid digital first image, and

 

obtaining as said second digital image a mirror image of said digital first image.

36. The method of claim 35, further comprising performing lateral inclination correction on said digital first image prior to obtaining said mirror image.

37. A method of computerized processing of chest images, comprising:

obtaining digital first and second images of a chest including internal rib edges of ribs located interior of a rib cage and overlaying a lung;

selecting template regions of interest (ROIs) in one of said first and second images and search area ROIs in an other of said first and second images;

determining cross correlation values between pixels in said template ROIs and said search area ROIs;

determining shift vectors for said template ROIs, comprising,

determining local shift vectors between pixels in said search ROIs relative to said template ROIs,

determining internal and external energies based on smoothness of said local shift vectors and said cross correlation values, respectively, and

modifying said local shift vectors based at least in part on said internal and external energies;

producing said shift vectors based on said modified local shift vectors; and

warping one of said first and second images to produce a warped image which is registered to said other of said first and second images based at least in part on said shift vectors; and

wherein said step of obtaining digital first and second images comprises,

obtaining said digital first image, and

obtaining as said second image a mirror image of said digital first image.

38. The method of claim 37, further comprising performing lateral inclination correction on said digital first image prior to obtaining said mirror image.