Method and apparatus for automatic shape characterization

• US 6,611,630 B1
• Filed: 06/07/1999
• Issued: 08/26/2003
• Est. Priority Date: 07/10/1996
• Status: Expired due to Term

• Current Assignee: Brown University, Washington University In St Louis
• Original Assignee: Brown University, Washington University In St Louis
• Inventors: Joshi, Sarang, Csenansky, John G., Haller, John W., Miller, Michael I., Grenander, Ulf
• Primary Examiner(s): COUSO, YON JUNG
• Application Number:
  US09/326,657
• Time in Patent Office: 1,541 Days
• Field of Search: 382/128, 382/159, 382/224, 382/131, 382/276, 382/286, 382/293, 600/300
• US Class Current: 382/293
• CPC Class Codes: G06F 18/28 Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 3/153 using elastic snapping
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06V 10/46 Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features colour feature extraction G06V10/56
• CPC Class Codes: G06V 10/754 involving a deformation of the sample pattern or of the reference pattern; Elastic matching
• CPC Class Codes: G06V 10/76 based on eigen-space representations, e.g. from pose or different illumination conditions; Shape manifolds

Abstract

A method and apparatus for classifying population states based on shape characterizations of sub-manifolds of points, curves, surfaces, or sub-volumes. A structure is examined using, for example, clinical imaging techniques such as CT, MRI, or Ultrasound. The image is then subjected to a transform function to generate a map of the new image. The new image map which contains information regarding the shape of the structure is compared to average shapes characterizing population groups. If the shape of the new image falls within a best match probability with an average shape, the new image is classified as a member of the population characterized by the average shape. Each population represents a specific classification state.

Thus, if the shape of the new structure resembles the average shape of a population group, the new shape is classified as the same population state as the other structures displaying the same shape characteristics.

Claims

1. A method for building a statistical shape model for a class of subjects using shapes in images of the subjects comprising the steps of:

generating an image coordinate map from a volume matching transform relating a template image to an image of one of the subjects in the class;

computing a shape measure for a structure of interest from said image coordinate map;

repeating said generating and computing steps for a plurality of subjects in the class; and

building a statistical shape model for said class from said computed shape measures.

2. A method for building a statistical shape model for a class of subjects using shapes in images of the subjects comprising the steps of:

generating an image coordinate map from a volume matching transform relating a template image to an image of one of the subjects in the class;

restricting said image coordinate map to a structure of interest in said image of one of the subjects;

computing a shape measure for said structure of interest from said image coordinate map;

repeating said generating, restricting, and computing steps for a plurality of subjects in the class; and

building a statistical shape model for said class from said computed shape measures.

3. The method of claim 2, wherein the step of restricting said image coordinate map to a structure of interest includes the substep of:

identifying a point, line, surface, or subvolume in said image of one of the subjects.

4. The method of claim 2, wherein the step of building a statistical shape model includes the substep of:

computing a probability distribution function representing said class.

5. A method for classifying a subject based on a shape in an image of the subject comprising the steps of:

generating an image coordinate map from a volume matching transform relating a template image to a subject image;

restricting said image coordinate map to a structure of interest in said image of one of the subjects;

computing a shape measure for a structure of interest from said image coordinate map;

comparing said shape measure to a statistical shape model representing a first class of subjects;

comparing said shape measure to a statistical shape model representing a second class of subjects; and

classifying said subject as belonging to said first class or said second class according to the result of said shape measure comparisons.

6. The method of claim 5, wherein the step of restricting said image coordinate map to a structure of interest includes the substep of:

identifying a point, line, surface, or subvolume in said image of one of the subjects.

7. The method of claim 6, including the substep of generating a database by storing built statistical shape models that characterize different classes.

8. The method of claim 7, including the substep of comparing said computed shape measure of a new image to the statistical shape models of each different class of the database.

9. The method of claim 8, wherein the step of computing a shape measure for detailed high dimensional comparisons includes the substep of:

computing a complete orthonormal expansion of the image coordinate map using basis vectors.

10. The method of claim 9, further including comparing the computed shape measure to the shape measure of each statistical shape model shape measure stored in the database.

11. The method of claim 8, wherein the step of computing a shape measure for scale groups comparisons includes the substep of:

computing an expansion of the map using polar decomposition.

12. The method of claim 11, further including comparing the computed shape measure to the shape measure of each model shape measure stored in the database.

13. The method of claim 8, wherein the step of computing a shape measure for flip symmetry comparisons includes the substep of:

computing an expansion of the map using polar decomposition with a determinant of −

1.

14. The method of claim 13, further including comparing the computed shape measure to the shape measure of each model shape measure stored in the database.

15. The method of claim 8, wherein one of said comparing steps includes the substep of:

computing a Bayesian hypothesis test.

16. A method for classifying a subject based on a shape in an image of the subject comprising the steps of:

generating an image coordinate map from a volume matching transform relating a template image to a subject image;

computing a shape measure for a structure of interest from said image coordinate map;

comparing said shape measure to a statistical shape model representing a first class of subjects;

comparing said shape measure to a statistical shape model representing a n-th class of subjects; and

classifying said subject as belonging to anyone of first through n-th class according to the result of said shape measure comparisons.

17. A method for building a statistical shape model for a class of subjects having a disease using shapes in medical images of the subjects comprising the steps of:

generating an image coordinate map from a volume matching transform relating a template image to an image of one of the subjects in the class;

restricting said image coordinate map to an anatomical structure of interest in said image of one of the subjects;

computing a shape measure for said anatomical structure of interest from said image coordinate map;

repeating said generating, restricting, and computing steps for a plurality of subjects in the class; and

building a statistical shape model for said class from said computed shape measures.

18. A method for classifying a disease state of a subject based on a shape of an anatomical region in an image of the subject comprising the steps of:

generating a map from a volume matching transform relating a template image to a subject image;

restricting said map to an anatomical structure of interest in said subject image;

computing a shape measure for said structure of interest from said map;

comparing said shape measure to a model representing a first class of subjects having one type of disease;

comparing said shape measure to a model representing a second class of subjects having another type of disease; and

classifying the disease state of said subject as belonging to said first class or said second class according to the result of said shape measure comparisons.

19. A method for classifying a disease state of a subject based on a shape of an anatomical region in an image of the subject comprising the steps of:

generating a map from a volume matching transform relating a template image to a subject image;

restricting said map to an anatomical structure of interest in said subject image;

computing a shape measure for said structure of interest from said map;

comparing said shape measure to a model representing a first class of subjects having one type of disease;

comparing said shape measure to a model representing an n-th class of subjects having another type of disease; and

classifying the disease state of said subject as belonging to any one of said first through n-th class according to the result of said shape measure comparisons.

20. An apparatus for building a statistical shape model for a class of subjects using shapes in images of the subjects comprising:

a first computer processing unit for generating an image coordinate map from a volume matching transform relating a template image to an image of one of the subjects in the class;

a first computer restricting unit for restricting said image coordinate map to a structure of interest in said image of one of the subjects;

a second computer processing unit for computing a shape measure for said structure of interest from said image coordinate map; and

a database for building a statistical shape model for said class from said computed shape measures.

21. The apparatus of claim 20, wherein the first computer restricting unit for restricting said an image coordinate map to a structure of interest, further comprises:

an identification unit for identifying a point, line, surface, or subvolume in said image of one of the subjects.

22. The apparatus of claim 20, wherein said second processing unit for computing a shape measure, further comprises:

a microprocessor for computing a complete orthonormal expansion of the image coordinate map using basis vectors.

23. The apparatus of claim 20, wherein said second processing unit for computing a shape measure, further comprises:

a microprocessor for computing an expansion of the map using polar decomposition.

24. The apparatus of claim 20, wherein said second processing unit for computing a shape measure, further comprises:

a microprocessor for computing an expansion of the map using polar decomposition with a determinant of −

1.

25. The apparatus of claim 20, wherein the database for building a statistical shape model, further comprises:

a third computer processing means for computing a probability distribution function representing said class.

26. An apparatus for classifying a subject based on a shape in an image of the subject comprising:

a first computer processor for generating an image coordinate map from a volume matching transform relating a template image to a subject image;

a first computer restricting unit for restricting said image coordinate map to a structure of interest in said subject image;

a second computer processor for computing a shape measure for said structure of interest from said image coordinate map;

a first comparator for comparing said shape measure to a statistical shape model representing a first class of subjects;

a second comparator for comparing said shape measure to a statistical shape model representing a second class of subjects;

an n-th comparator for comparing said shape measure to a statistical shape model representing an n-th class of subjects; and

a classification unit for classifying said subject as belonging to any one of said first through n-th class according to the result of said shape measure comparisons.

27. The apparatus of claim 26, wherein the first computer restricting unit for restricting said image coordinate map to a structure of interest, further comprises:

an identification unit for identifying a point, line, surface, or subvolume in said image of one of the subjects.

28. The apparatus of claim 26, wherein the second computer processing unit for computing a shape measure, further comprises:

a microprocessor for computing a complete orthonormal expansion of the image coordinate map using basis vectors.

29. The apparatus of claim 26, wherein said second computer processing unit for computing a shape measure, further comprises:

a microprocessor for computing an expansion of the map using polar decomposition.

30. The apparatus of claim 26, wherein said second computer processing unit for computing a shape measure, further comprises:

a microprocessor for computing an expansion of the map using polar decomposition with a determinant of −

1.

31. The apparatus of claim 26, wherein said first comparator for comparing said shape measure to a statistical shape model representing classes of shapes, further comprises:

a microprocessor for computing a Bayesian hypothesis test.

32. The apparatus of claim 26, wherein said second comparator for comparing said shape measure to a statistical shape model representing classes of shapes, further comprises:

a microprocessor for computing a Bayesian hypothesis test.

33. The apparatus of claim 26, wherein said n-th comparator for comparing said shape measure to a statistical shape model representing classes of shapes, further comprises:

a microprocessor for computing a Bayesian hypothesis test.

34. An apparatus for building a statistical shape model for a class of subjects having a disease using shapes in medical images of the subjects comprising:

a first computer processor for generating an image coordinate map from a volume matching transform relating a template image to an image of one of the subjects in the class;

a first computer restricting unit for restricting said image coordinate map to an anatomical structure of interest in said image of one of the subjects;

a second computer processor for computing a shape measure for said anatomical structure of interest from said image coordinate map;

a repeating means for repeating said generating, restricting, and computing steps for a plurality of subjects in the class; and

a database for building a statistical shape model for said class from said computed shape measures.

35. An apparatus for classifying a disease state of a subject based on a shape of an anatomical region in an image of the subject comprising:

a first computer processor for generating a map from a volume matching transform relating a template image to a subject image;

a first computer restricting unit for restricting said map to an anatomical structure of interest in said subject image;

a second computer processor for computing a shape measure for said structure of interest from said map;

a first comparator for comparing said shape measure to a model representing a first class of subjects having one type of disease;

a second comparator for comparing said shape measure to a model representing a second class of subjects having another type of disease; and

a classification unit for classifying the disease state of said subject as belonging to said first class or said second class according to the result of said shape measure comparisons.

36. A computer program product for use in a computer adapted for building a statistical shape model for a class of subjects using shapes in images of the subjects, the computer program product comprising a computer readable medium for storing computer readable code means, which when executed by the computer, enables the computer to build a statistical shape model, and wherein the computer readable code means includes computer readable instructions for causing the computer to execute a method comprising the steps of:

generating an image coordinate map from a volume matching transform relating a template image to an image of one of the subjects in the class;

computing a shape measure for a structure of interest from said image coordinate map;

repeating said generating and computing steps for a plurality of subjects in the class; and

building a statistical shape model for said class from said computed shape measures.

37. A computer program product for use in a computer adapted for building a statistical shape model for a class of subjects using shapes in images of the subjects, the computer program product comprising a computer readable medium for storing computer readable code means, which when executed by the computer, enables the computer to build a statistical shape model, and wherein the computer readable code means includes computer readable instructions for causing the computer to execute a method comprising the steps of:

generating an image coordinate map from a volume matching transform relating a template image to an image of one of the subjects in the class;

restricting said image coordinate map to a structure of interest in said image of one of the subjects;

computing a shape measure for said structure of interest from said image coordinate map;

repeating said generating, restricting, and computing steps for a plurality of subjects in the class; and

building a statistical shape model for said class from said computed shape measures.

38. A computer program product for use in a computer adapted for classifying a subject based on a shape in an image of the subject, the computer program product comprising a computer readable medium for storing computer readable code means, which when executed by the computer, enables the computer to classify a subject, and wherein the computer readable code means includes computer readable instructions for causing the computer to execute a method comprising the steps of:

generating an image coordinate map from a volume matching transform relating a template image to a subject image;

restricting said image coordinate map to a structure of interest in said image of one of the subjects;

computing a shape measure for a structure of interest from said image coordinate map;

comparing said shape measure to a statistical shape model representing a first class of subjects;

comparing said shape measure to a statistical shape model representing a second class of subjects; and

classifying said subject as belonging to said first class or said second class according to the result of said shape measure comparisons.