A Unified Feature Registration Framework for Brain Anatomical Alignment

1. A Unified Feature RegistrationFramework 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. Distribution Before Alignment Direct Comparison of Subjects Brain Function Image Alignment of Subjects Distribution After Alignment Comparison of Subjects After Alignment Studying Function-Structure Connection

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 Extraction Extracted Point Features

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

7. Feature Extraction Feature Fusion Outer Cortex Surface Point Feature Representation Feature Matching All Features Subject I Major Sulcal Ribbons Point Feature Representation Subject II A Unified Feature Registration Method

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

13. Matching JCM Energy Function Point Set X Point Set Y Clustering Clustering Clusters Center Set V Cluster Center Set U 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. Two types of features investigated: • Outer cortex surface. • Major sulcal ribbons. • Comparison of different methods: Method I Method II Method III Comparison of Different Features • Different features can be used in our approach.

18. Change the choice of features to compare method I, II and III True Deformation (GRBF) Target Template Feature Matching Error Evaluation Estimated Deformation (TPS) Template Recovery Synthetic Study Setup

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

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

33. Example Application: Face Matching