A meta registration framework for lesion matching.
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.
Seshamani
Sharmishtaa
S
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. sharmi@jhu.edu
Rajan
Purnima
P
Kumar
Rajesh
R
Girgis
Hani
H
Dassopoulos
Themos
T
Mullin
Gerard
G
Hager
Gregory
G
D000465:Algorithms; D003424:Crohn Disease / Q000473:pathology*; D016099:Endoscopy, Gastrointestinal / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique