title: A meta method for image matching.
abstract: This paper presents a novel system for image matching in optical endoscopy. The proposed metamatching system approaches the challenge of matching images in a complex scene by incorporating multiple matchers and a decision function. Experiments are presented for Crohn's disease lesion matching in capsule endoscopy with a metamatcher consisting of five independent matchers. We compare the performance of six different types of decision functions. Results show that the F-measure of the metamatching system containing all five matchers is 4%-7% greater than the performance of using the best matcher only, with a maximum F-measure of 0.811. The robustness of the method is validated using simulated data generated by controlled deformations of the image. We also demonstrate how the addition of simulated data to the training set can be used to augment the performance of the metamatcher by up to 10%.
authors.affiliation: Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA. sharmi@cs.jhu.edu
authors.forename: Sharmishtaa
authors.initials: S
authors.lastname: Seshamani
authors.forename: Rajesh
authors.initials: R
authors.lastname: Kumar
authors.forename: Gerard
authors.initials: G
authors.lastname: Mullin
authors.forename: Themistocles
authors.initials: T
authors.lastname: Dassopoulos
authors.forename: Gregory D
authors.initials: GD
authors.lastname: Hager
mesh_terms: D000465:Algorithms; D053704:Capsule Endoscopy / Q000379:methods*; D003424:Crohn Disease / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results