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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

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A Unified Feature Registration Framework for Brain Anatomical Alignment

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A unified feature registration framework for brain anatomical alignment
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


Brain anatomical alignment
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.


Studying function structure connection

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


Inter subject brain registration
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
Previous Work: 3D Sulcal Point Matching

Feature Extraction

Extracted Point Features


Previous work 3d sulcal point matching1

After TPS alignment:

Previous Work: 3D Sulcal Point Matching

Overlay of 5 subjects before TPS alignment:


A unified feature registration method

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



Unification of different features
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.



Overcome sub sampling problem
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

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.


Jcm energy function

Matching

JCM Energy Function

Point Set X

Point Set Y

Clustering

Clustering

Clusters Center Set V

Cluster Center Set U

Annealing:


Jcm energy function1
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.


Jcm example
JCM Example

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



Comparison of different features

  • Comparison of different methods:

Method I

Method II

Method III

Comparison of Different Features

  • Different features can be used in our approach.


Synthetic study setup

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


Results method i vs method iii
Results: Method I vs. Method III 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
Results: Method II vs. Method III 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
Conclusion III

  • 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
Acknowledgements III

  • 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.



Estimating an average shape
Estimating An Average Shape III

  • 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.


Super clustering matching algorithm scm

Point Set X III

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:


A unified feature registration framework for brain anatomical alignment
End III

  • Further Information:

    • Web site: http://noodle.med.yale.edu/~chui/


A unified feature registration framework for brain anatomical alignment

End III



A unified feature registration framework for brain anatomical alignment

Point Matching III

Example Application: Face Matching


A unified feature registration framework for brain anatomical alignment

Example Application: IIIFace Matching


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