Non-rigid motion image analysis

• US 7,043,063 B1
• Filed: 07/19/2000
• Issued: 05/09/2006
• Est. Priority Date: 08/27/1999
• Status: Expired due to Term

• Current Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Original Assignee: Mirada Solutions Ltd. (Siemens AG)
• Inventors: Noble, Julia Alison, Jacob, Gary
• Primary Examiner(s): Werner, Brian
• Assistant Examiner(s): LAVIN, CHRISTOPHER L
• Application Number:
  US10/069,291
• Time in Patent Office: 2,120 Days
• Field of Search: 382/128, 382/107, 382/131, 382/132, 382/173, 382/180, 382/190, 382/192, 382/199
• US Class Current: 382/128
• CPC Class Codes: A61B 6/508 for non-human patients
• CPC Class Codes: G06T 2207/10132 Ultrasound image
• CPC Class Codes: G06T 2207/30048 Heart; Cardiac
• CPC Class Codes: G06T 7/0016 involving temporal comparison
• CPC Class Codes: G06T 7/246 using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06T 7/251 involving models

Abstract

A method of analysing a sequence of images of a deformable object in non-rigid motion involves modeling the boundary using a non-rigid contour. A representation of movement of the contour through the sequence of images is calculated using a tracking space shape. The calculated movement representation is decomposed using an interpretational space shape that is different than the tracking space shape.

Claims

1. A method of analysing a sequence of images of a deformable object in non-rigid motion, comprising:

detecting the boundary of the object in each image of the sequence and fitting a non-rigid contour to the detected boundary;

tracking the boundary through the sequence by calculating a shape-space representation with respect to a first shape-space of the movement of the non-rigid contour through the sequence;

defining a second, different shape-space whose shape vector comprises components corresponding to a desired attribute of the motion; and

calculating from the tracked boundary the shape-vector corresponding to the different shape-space.

2. A method according to claim 1, wherein the non-rigid contour is a spline curve.

3. A method according to claim 2, further comprising visually locating the boundary in only some selected images in the sequence and fitting the spline curve to the visually located boundary in each selected image by calculation of the control points for the spline curve.

4. A method according to claim 3, wherein the first shape-space representation represents the movement of the spline curve control points through the selected images.

5. A method according to claim 3, further comprising predicting the position of the boundary in each frame of the sequence based on the spline curve, detecting image features representative of the boundary in the vicinity of the predicted position of the boundary, and correcting the predicted position on the basis of the detected image features.

6. A method according to claim 2, further comprising displaying the spline curve overlying the image.

7. A method according to claim 2, further comprising calculating and outputting for each of the segments an average of the amount of movement of control points of the spline curve for that segment.

8. A method according to claim 7, wherein the average is weighted in favour of spline curve control points in the middle of each segment.

9. A method according to claim 2, further comprising calculating and outputting for each of the segments a measure of the variation in the amount of movement of control points of the spline curve for that segment.

10. A method according to claim 2, further comprising calculating and outputting for each of the segments a measure of the maximal excursion of control points of the spline curve for that segment.

11. A method according to claim 2, further comprising from calculating control points of the spline curve the shape-vector corresponding to the different shape-space.

12. A method according to claim 11, wherein a pseudo-inverse of the different shape-space space is defined to produce as components of the shape-vector a measure of the movement of the spline curve control points for each of the segments.

13. A method according to claim 12, further comprising displaying graphically the variation through the sequence of the shape-vector components.

14. A method according to claim 2, wherein four spline function control points are defined for each of the segments.

15. A method according to claim 1, wherein the shape-space is calculated by performing a principal component analysis (PCA) of the movement of the boundary through the selected images.

16. A method according to claim 1 wherein the boundary is an inner boundary of a wall of the object, and the method further comprises:

searching outside the inner boundary for image features representing an outer boundary of the wall of the object.

17. A method according to claim 16, further comprising fitting a spline curve to the detected image features representing the outer boundary.

18. A method according to claim 17, wherein the spline curve is fitted by:

manually locating the inner and outer boundaries in only some images of the sequence;

calculating a shape-space for the change through the sequence of the distance between the two boundaries;

detecting the inner boundary and performing the search outside the inner boundary for image features representing the outer boundary in images of the sequence; and

fitting a spline curve to the detected image features in the other images of the sequence by using the shape-space.

19. A method according to claim 18, further comprising performing a principal component analysis of the change in the distance between the two boundaries, as a basis for the shape-space.

20. A method according to claim 18, wherein, when fitting the spline curve to the detected image features, the detected image features are weighted down if they imply a difference between the inner and outer boundaries which lies outside the shape-space space for that difference.

21. A method according to claim 17, wherein, when fitting the spline curve to the detected image features, the detected image features are weighted down if they imply a high curvature of the outer boundary.

22. A method according to claim 16, wherein the searching outside the inner boundary for image features representing the outer boundary comprises detecting and analysing changes in the image intensity outwards from the inner boundary.

23. A method according to claim 22, further comprising detecting a ridge in a plot of the image intensity outwards from the inner boundary.

24. A method according to claim 23, further comprising performing a wavelet decomposition of the plot of the image intensity to smooth the plot and detecting as the ridge a maximum in the smoothed plot.

25. A method according to claim 22, wherein the search is conducted along a plurality of search lines spaced along and extending radially outwardly from the said inner boundary.

26. A method according to claim 16, wherein the images are ultrasound images.

27. A method according to claim 16, wherein the object is a human or animal organ.

28. A method according to claim 16, wherein the object is a human or animal heart.

29. A method according to claim 28, wherein the object is the left or right ventricle.

30. A method according to claim 28, further comprising graphically displaying the change through the sequence of the distance between the inner and outer boundaries as a representation of myocardial thickening.

31. A method according to claim 28, further comprising segmenting the wall of the heart and graphically displaying for each segment the change through the sequence of the distance between the inner and outer boundaries as a representation of myocardial thickening for that segment.

32. A method according to claim 31, wherein the distance between the inner and outer boundaries is averaged or integrated for within each segment.

33. A method according to claim 31, further comprising calculating the variation within each segment of the change through the sequence of the distance between the inner and outer boundaries.

34. A computer program storage medium readable by a computer system and tangibly embodying a computer program comprising computer-executable instructions for performing the method of claim 1.

35. A method according to claim 1, wherein the components of the shape-vector correspond to the movement of different segments of the detected boundary.

36. A method according to claim 35, further comprising of displaying graphically the calculated amount of movement of each of the segments of the detected boundary.

37. A method according to claim 35, further comprising calculating and outputting for each of the segments of the detected boundary an average of the amount of movement of that segment.

38. A method according to claim 35, further comprising calculating for each of the segments of the detected boundary the variation in the amount of movement within that segment.

39. A method according to claim 35, further comprising calculating for each of the segments the maximal excursion of the detected boundary during the non-rigid motion.

40. A method according to claim 35, wherein the object is an internal body organ of a human or animal and the segments are clinically-significant segments of the organ.

41. A method according to claim 40, wherein the images are produced by a technique selected from the group consisting of:

ultrasound-based, MR-based and x-ray based, imaging and nuclear medicine.

42. A method of analysing a sequence of images of an internal body organ in non-rigid motion, comprising:

detecting the boundary of the organ in each image of the sequence; and

automatically calculating the amount of movement through the sequence of each of a plurality of clinically significant segments of the detected boundary,

wherein a spline curve is fitted to the boundary and the method further comprises calculating and outputting for each of the clinically significant segments an average of the amount of movement of the spline curve control points for that segment, the average being weighted in favour of spline curve control points in the middle of each segment.

43. A method of analysing a sequence of images of a deformable object in non-rigid motion to detect inner and outer boundaries of a wall of the object, the method comprising:

detecting the inner boundary; and

searching outside the inner boundary for image features representing the outer boundary,

wherein the method further comprises fitting a spline curve to the detected image features representing the outer boundary,

wherein the spline curve is fitted by;

manually locating the inner and outer boundaries in only some images of the sequence;

calculating a shape-space for the change through the sequence of the distance between the two boundaries;

detecting the inner boundary and performing the search outside the inner boundary for image features representing the outer boundary in images of the sequence; and

fitting a spline curve to the detected image features in the other images of the sequence by using the shape-space; and

wherein, when fitting the spline curve to the detected image features, the detected image features are weighted down if they imply a difference between the inner and outer boundaries which lies outside the shape-space for that difference.

44. A method of analysing a sequence of images of a deformable object in non-rigid motion, the method comprising:

modeling the boundary using a non-rigid contour;

calculating a representation of movement of the contour through the sequence of images using a tracking shape-space; and

decomposing the calculated movement representation using an interpretational shape-space shape space that is different than the tracking shape-space.

45. A method according to claim 44, wherein the non-rigid contour is a spline curve.

46. A method according to claim 44, further comprising:

displaying the decomposed movement representation.

47. A method according to claim 44, further comprising:

generating numerical values corresponding to the decomposed movement representation.

48. A computer-readable medium on which computer-executable instructions for implementing the method of claim 44 are tangibly embodied.