Detecting registration failure

Authors:
S. Seshamani;R. Kumar;P. Rajan;S. Bejakovic;G. Mullin;T. Dassopoulos;G. Hager
Affiliations:
Department of Computer Science, Johns Hopkins University, Baltimore, MD and Washington University, School of Medicine St. Louis, MO;Department of Computer Science, Johns Hopkins University, Baltimore, MD and Washington University, School of Medicine St. Louis, MO;Department of Computer Science, Johns Hopkins University, Baltimore, MD and Washington University, School of Medicine St. Louis, MO;Department of Computer Science, Johns Hopkins University, Baltimore, MD and Washington University, School of Medicine St. Louis, MO;Department of Computer Science, Johns Hopkins University, Baltimore, MD and Washington University, School of Medicine St. Louis, MO;Department of Computer Science, Johns Hopkins University, Baltimore, MD and Washington University, School of Medicine St. Louis, MO;Department of Computer Science, Johns Hopkins University, Baltimore, MD and Washington University, School of Medicine St. Louis, MO
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Abstract

This paper presents a new approach to evaluation of registration using a general discriminative learning model that is independent of the type of registration method. We select features by association of a registration with a set of metrics (pixel based, patch based and histogram based statistics) and learn a classifier that discriminates mis-registrations from correct registrations using Adaboost. Experiments on a set of wireless capsule endoscopy (CE) images and images extracted from minimally invasive surgical endoscopic video data are presented. Results show that the proposed method outperforms any single classifier.