Serial nonrigid vascular registration using weighted normalized mutual information

Authors:
J. W. Suh;D. Scheinost;X. Qian;A. J. Sinusas;C. K. Breuer;X. Papademetris
Affiliations:
Diagnostic Radiology, University of South Florida;Diagnostic Radiology, University of South Florida;Department of Computer Science and Engineering, University of South Florida;Internal Medicine, University of South Florida;Department of Surgery, University of South Florida;Diagnostic Radiology, University of South Florida and Biomedical Engineering, Yale University
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

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Abstract

Vascular registration is a challenging problem with many potential applications. However, registering vessels accurately is difficult as they often occupy a small portion of the image and their relative motion/deformation is swamped by the displacements seen in large organs such as the heart and the liver. Our registration method uses a vessel detection algorithm to generate a vesselness image (probability of having a vessel at any given voxel) which is used to construct a weighting factor that is used to modify the intensity metric to give preference to vascular structures while maintaining the larger context. Therefore, our proposing method uses fully data-driven calculated weights and needs no prior knowledge for the weight calculation. We applied our method to the registration of serial MRI lamb images obtained from studies on tissue engineered vascular grafts and demonstrate encouraging performance as compared to non-weighted registration methods.