Post on 19-Dec-2015
Unified Joint Feature Registration for Brain Anatomical Alignment
Haili Chui, Robert Schultz, Lawrence Win, James Duncan and Anand Rangarajan
Image Processing and Analysis GroupDepartments of Electrical Engineering
Yale University
Brain Anatomical Alignment• Brains are different:
– Shape.– Structure.
• Direct comparison of brains between different subject 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.
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
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.
Previous Work: 3D Sulcal Point Matching
Feature Extraction Extracted Point Features
Previous Work: 3D Sulcal Point Matching
Overlay of 5 subjects before TPS alignment:
After TPS alignment:
A Unified Feature Registration Method
Outer Cortex Surface
Major Sulcal Ribbons
All FeaturesPoint Feature
Representation
Point Feature Representation
Feature Extraction Feature Fusion
Feature
Matching
Subject I
Subject II
Non-rigid Feature Point Registration
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.
Joint Clustering-Matching Algorithm (JCM)
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.
Joint Clustering-Matching Algorithm (JCM)
• JCM:
• Reduce computational cost using sub-sampled cluster centers.
• Accomplish optimal cluster placement through joint cluster-matching.
• Symmetric: two way matching.
MatchingClusters Center Set V
Clustering
Cluster Center Set U
Clustering
Point Set X Point Set YOriginal RPM
• Diagram:
JCM Energy Function
MatchingClusters Center Set V
Clustering
Cluster Center Set U
Clustering
Point Set X Point Set Y
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JCM Energy Function
• Fuzzy assignment + least squares energy function:
• Row and column summation constraints.
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JCM Example
• Matching 2 face patterns with JCM (click to play movie).
Experiments
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 I Method II Method III
Synthetic Study Setup
Template True Deformation (GRBF)
Target
Template RecoveryEstimated Deformation
(TPS)
Error Evaluation
Feature Matching
Change the choice of features to
compare method I, II and III
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.
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.
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.
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.
• Financial support is provided by the grants from the Whitaker Foundation, NSF, NIH.
Future Work
Estimating An Average Shape
• Given multiple sample shape (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.
“Super” Clustering-Matching Algorithm (SCM)
• Diagram:
MatchingMatchable
ClustersOutlier 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
End
• Further Information:– Web site: http://noodle.med.yale.edu/~chui/
End
2D Examples of RPM
Point Matching
Example Application: Face Matching
Example Application: Face Matching