Automated surface-based anatomical analysis based on atlas-based segmentation of medical imaging

1. A non-invasive imaging system, comprising:

a non-invasive imaging scanner; and

a computer comprising:

a signal processing unit in communication with said imaging scanner to receive imaging data of a tissue region from a subject under observation; and

a data storage unit in communication with said signal processing unit, wherein said data storage unit stores template data corresponding to said tissue region of said subject, the template data of said subject comprising a plurality of template substructures,

wherein said signal processing unit is adapted to:

transform the template data to fit said plurality of template substructures to the imaging data through a shape transformation;

calculate parameters of the shape transformation for at least one template substructure; and

generate a surface map of said at least one of said template substructures based on said calculated parameters,

wherein said surface map encodes a measure of shape differences between said template data and said tissue region.

2. The imaging system according to claim 1 , further comprising:

a display apparatus in communication with at least one of said signal processing unit and said data storage unit to display an analyzed deformation pattern.

3. The imaging system according to claim 1 , wherein said surface map is modeled as a linear combination of at least one spatially localizing basis function encoding spatial shape variation information of a subvolume of said tissue region.

4. The imaging system according to claim 3 , wherein said signal processing unit is further adapted to:

extract a principal component of said linear combination of at least one spatially localizing basis function.

5. The imaging system according to claim 3 , wherein said signal processing unit is further adapted to:

compare said linear combination determined for said subvolume of said tissue region from said subject with a statistical database of said linear combination previously determined for said subvolume of said tissue region from one or more groups of subjects; and

determine if said subject statistically belongs to said one or more groups of subjects.

6. The imaging system according to claim 1 , wherein said imaging scanner is configured to scan the tissue region that is at least one of a brain, a heart, a liver, or other internal organ of said subject under observation.

7. The imaging system according to claim 1 , wherein said data storage unit is configured to store said template data that incorporates at least one image from at least one subject that is different from said subject under observation.

8. The imaging system according to claim 1 , wherein said data storage unit is configured to store said template data that incorporates at least one image from a previous scan of said subject under observation.

9. The imaging system according to claim 1 , wherein said signal processing unit is at least partially incorporated in a workstation that is structurally separate and in communication with said imaging scanner.

10. The imaging system according to claim 1 , wherein said signal processing unit is at least partially incorporated within a structure housing said imaging scanner.

11. The system of claim 1 , wherein the shape transformation comprises a large deformation diffeomorphic metric mapping.

12. The system of claim 11 , wherein the calculated parameters include momentum of the transformed template data.

13. The system of claim 12 , wherein the surface map comprises a normal surface momentum map encoding a boundary shape based on the calculated momentum of the transformed template data.

14. The system of claim 12 , wherein the calculated parameters are geodesic connection properties of the template data to the imaging data, the geodesic connection properties including a conservation of momentum property and a normality property.

15. A method of processing images on a computer, comprising:

receiving imaging data of a tissue region of a subject under observation on a non-invasive imaging system;

receiving template data corresponding to said tissue region of the subject under observation, the template data of said subject comprising a plurality of template substructures;

transforming the template data to fit said plurality of template substructures to the imaging data through a shape transformation;

calculating parameters of the shape transformation for at least one template substructure; and

generating a surface map of said at least one of said substructures using the computer based on said calculated parameters,

wherein said surface map encodes a measure of shape differences between said template data and said tissue region.

16. The method according to claim 15 , wherein said template data is received from at least one of the non-invasive imaging system, one or more data storage devices, a software simulation executed by one or more processors, or combinations thereof.

17. The method according to claim 15 , wherein said surface map is modeled as a linear combination of at least one spatially localizing basis function encoding spatial shape variation information of a subvolume of said tissue region.

18. The method according to claim 17 , wherein said localizing basis function is a Laplace-Beltrami (LB) function.

19. The method according to claim 17 , further comprising:

comparing said linear combination determined for said subvolume of said tissue region in said imaging data from said subject with a statistical database of said linear combination previously determined for said subvolume of said tissue region in existing images from one or more groups; and

determining if said imaging data is statistically correlated with said existing images from said one or more groups.

20. The method according to claim 17 , further comprising:

extracting a principal component of said linear combination of at least one spatially localizing basis function.

21. The method according to claim 20 , further comprising:

extracting a first set of principal components of said linear combination modeling a first set of normal surface momentum maps that encode said property of said subvolume of said tissue region in a first plurality of images from a first plurality of subjects;

extracting a second set of principal components of said linear combination modeling a second set of normal surface momentum maps that encode said property of said subvolume of said tissue region in a second plurality of images from a second plurality of subjects; and

determining a statistical difference between the extracted first and second set of principal components.

22. The method according to claim 21 , wherein determining said statistical difference comprises signifying a physiological difference between said first and second plurality of subjects.

23. The method of claim 15 , wherein the shape transformation comprises a large deformation diffeomorphic metric mapping.

24. The method of claim 23 , wherein the calculated parameters include momentum of the transformed template data.

25. The method of claim 24 , wherein the surface map comprises a normal surface momentum map encoding a boundary shape based on the calculated momentum of the transformed template data.

26. The method of claim 24 , wherein the calculated parameters are geodesic connection properties of the template data to the imaging data, the geodesic connection properties including a conservation of momentum property and a normality property.

27. A non-transitory computer-readable medium containing software, which software when executed by a computer, causes the computer to implement the method according to claim 15 .