1. A Unified Feature Registration Framework for Brain Anatomical Alignment Haili Chui, Robert Schultz, Lawrence Win, James Duncan and Anand Rangarajan* Image Processing and Analysis Group Departments of Electrical Engineering and Diagnostic Radiology Yale University *Department of Computer & Information Science and Engineering University of Florida

2. Brain Anatomical Alignment Brains are different: –Shape. –Structure. Direct comparison of brains between different subjects is not very accurate. Statistically and quantitatively more accurate study requires the brain image data to be put in a common “normalized” space through alignment. Examples of areas that need brain registration: –Studying structure-function connection. –Tracking temporal changes. –Generating probabilistic atlases. –Creating deformable atlases.

3. Studying Function-Structure Connection Brain Function Image Alignment of Subjects Comparison of Subjects After Alignment Direct Comparison of Subjects Distribution Before Alignment Distribution After Alignment

4. Inter-Subject Brain Registration Inter-subject brain registration: –Alignment of brain MRI images from different subjects to remove some of the shape variability. Difficulties: –Complexity of the brain structure. –Variability between brains. Brain feature registration: –Choose a few salient structural features as a concise representation of the brain for matching. –Overcome complexity: only model important structural features. –Overcome variability: only model consistent features.

5. Previous Work: 3D Sulcal Point Matching Feature ExtractionExtracted Point Features

6. Previous Work: 3D Sulcal Point Matching Overlay of 5 subjects before TPS alignment: After TPS alignment:

7. A Unified Feature Registration Method Outer Cortex SurfaceMajor Sulcal RibbonsAll FeaturesPoint Feature Representation Feature ExtractionFeature Fusion Feature MatchingFeature Matching Subject I Subject II

8. Non-rigid Feature Point Registration

9. Unification of Different Features Ability to incorporate different types of geometrical features. –Points. –Curves. –Open surface ribbons. –Closed surfaces. Simultaneously register all features --- utilize the spatial inter- relationship between different features to improve registration.

10. Joint Clustering-Matching Algorithm (JCM)

11. Overcome Sub-sampling Problem Sub-sampling (e.g. clustering) reduces computational cost for matching. In-consistency problem with sub-sampling: The in-consistency can be overcome by sub-sampling (clustering) and matching simultaneously.

12. Joint Clustering-Matching Algorithm (JCM) JCM: Reduce computational cost using sub-sampled cluster centers. Accomplish optimal cluster placement through joint clustering and matching. Symmetric: two way matching. Matching Clusters Center Set V Clustering Cluster Center Set U Clustering Point Set X Point Set Y Original RPM Diagram:

13. JCM Energy Function Matching Clusters Center Set V Clustering Cluster Center Set U Clustering Point Set XPoint Set Y Annealing:

14. JCM Energy Function Clustering and regularization energy function: First two terms perform clustering, next four perform non-rigid matching and last two are entropy terms.

15. JCM Example Matching 2 face patterns with JCM (click to play movie).

16. Experiments

17. Comparison of Different Features Different features can be used in our approach. Two types of features investigated: –Outer cortex surface. –Major sulcal ribbons. Comparison of different methods: Method IMethod IIMethod III

18. Synthetic Study Setup TemplateTrue Deformation (GRBF) TargetTemplateRecoveryEstimated Deformation (TPS) Error Evaluation Feature Matching Change the choice of features to compare method I, II and III

19. Results: Method I vs. Method III Outer cortical surface alone can not provide adequate information for sub-cortical structures. Combination of two features works better.

20. Results: Method II vs. Method III Major sulcal ribbons alone are too sparse --- the brain structures that are relatively far away from the ribbons got poorly aligned. Combination of two features works better.

21. Conclusion Combination of different features improves registration. Unified brain feature registration approach: –Capable of estimating non-rigid transformations without the correspondence information. –General + unified framework. –Symmetric. –Efficient.

22. Acknowledgements Members of the Image Processing and Analysis Group at Yale University: –Hemant Tagare. –Lawrence Staib. –Xiaolan Zeng. –Xenios Papademetris. –Oskar Skrinjar. –Yongmei Wang. Colleagues in the brain registration project: –Joseph Walline. Partially supported is by grants from the Whitaker Foundation, NSF, and NIH.

23. Future Work

24. Estimating An Average Shape Given multiple sample shapes (sample point sets), compute the average shape for which the joint distance between the samples and the average is the shortest. Average ? Difficult if the correspondences between the sample points are unknown.

25. “Super” Clustering-Matching Algorithm (SCM) Diagram: Matching Matchable Clusters Outlier Cluster Clusters Center Set V Clustering Matchable Clusters Outlier Cluster Clusters Center Set U Clustering Point Set X Point Set Y Average Point Set Z Matching and Estimating

27. End Further Information: –Web site: http://noodle.med.yale.edu/~chui/

28. End

29. 2D Examples of RPM

32. Point Matching Example Application: Face Matching