A Meta Registration Framework for Lesion Matching

Autor:	Purnima Rajan, Sharmishtaa Seshamani, Hani Z. Girgis, Gerard E. Mullin, Rajesh Kumar, Gregory D. Hager, Themos Dassopoulos
Rok vydání:	2009
Předmět:	Matching (statistics) Pixel business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Task (project management) Feature based Medicine Decision function A priori and a posteriori Computer vision Artificial intelligence Image sensor business Multiple view
Zdroj:	Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009 ISBN: 9783642042676 MICCAI (1)
DOI:	10.1007/978-3-642-04268-3_72
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::158c630328d56835ed197f71a9a814d1 https://doi.org/10.1007/978-3-642-04268-3_72 Zobrazit plný text záznamu