Documentation/Nightly/Modules/SegmentationAidedRegistration

Introduction and Acknowledgements

This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the NA-MIC website. Author: Yi Gao, Brigham and Women's Hospital Contributor1: Josh Cates, University of Utah Contributor2: Liang-Jia Zhu, University of Alabama at Birmingham Contributor3: Alan Morris, University of Utah Contributor4: Danny Perry, University of Utah Contributor5: Greg Gardner, University of Utah Contributor6: Rob MacLeod, University Utah Contributor7: Sylvain Bouix, Brigham and Women's Hospital Contributor8: Allen Tannenbaum, University of Alabama at Birmingham Contact: Yi Gao, <email>gaoyi@bwh.harvard.edu</email>

National Alliance for Medical Image Computing (NA-MIC)   Surgical Planning Laboratory (SPL)   Scientific Computing and Imaging Institute (SCI)

Module Description

When we want to register two images, often times, we would like an accurate matching at certain region. For example, in the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized. In such cases, we can use the segmentation of the target region, atrium in the previous example, to aid the registration process. This module is such a segmentation aided registration tool.

Use Cases

• In the atrial fibrillation longitudinal study, we want to register the pre-op and post-op images. In particular, we want the matching at the atrium to be emphasized.

Tutorials

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Panels and their use

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Similar Modules

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References

• Y Gao, Y Rathi, S Bouix, A Tannenbaum; Filtering in the Diffeomorphism Group and the Registration of Point Sets; IEEE Transactions on Image Processing 21 (10), 4383--4396
• Y Gao, B Gholami, RS MacLeod, J Blauer, WM Haddad, A Tannenbaum; Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning. Proceedings of SPIE Medical Imaging 2010.