TY - GEN
T1 - A meta registration framework for lesion matching
AU - Seshamani, Sharmishtaa
AU - Rajan, Purnima
AU - Kumar, Rajesh
AU - Girgis, Hani
AU - Dassopoulos, Themos
AU - Mullin, Gerard
AU - Hager, Gregory
N1 - Funding Information:
Supported in part by National Institutes of Health with Grant 5R21EB008227-02 and Johns Hopkins University internal funds.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=79961178506&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79961178506&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-04268-3_72
DO - 10.1007/978-3-642-04268-3_72
M3 - Conference contribution
C2 - 20426035
AN - SCOPUS:79961178506
SN - 3642042678
SN - 9783642042676
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 582
EP - 589
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009 - 12th International Conference, Proceedings
T2 - 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009
Y2 - 20 September 2009 through 24 September 2009
ER -