A Meta Registration Framework for Lesion Matching

Authors:
Sharmishtaa Seshamani;Purnima Rajan;Rajesh Kumar;Hani Girgis;Themos Dassopoulos;Gerard Mullin;Gregory Hager
Affiliations:
Department of Computer Science, Johns Hopkins University, Baltimore;Department of Computer Science, Johns Hopkins University, Baltimore;Department of Computer Science, Johns Hopkins University, Baltimore;Department of Computer Science, Johns Hopkins University, Baltimore;School of Medicine St. Louis, Washington University;Johns Hopkins Hospital, Baltimore;Department of Computer Science, Johns Hopkins University, Baltimore
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Year:
2009

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Abstract

A variety of pixel and feature based methods have been proposed for registering multiple views of anatomy visible in studies obtained using diagnostic, minimally invasive imaging. A given registration method may outperform another depending on anatomical variations, imaging conditions, and imaging sensor performance, and it is often difficult a priori to determine the best registration method for a particular application. To address this problem, we propose a registration framework that pools the results of multiple registration methods using a decision function for validating registrations. We refer to this as meta registration. We demonstrate that our framework outperforms several individual registration methods on the task of registering multiple views of Crohn's disease lesions sampled from a Capsule Endoscopy (CE) study database. We also report on preliminary work on assessing the quality of registrations obtained, and the possibility of using such assessment in the registration framework.