Detecting registration failure

S. Seshamani, R. Kumar, P. Rajan, S. Bejakovic, G. Mullin, T. Dassopoulos, G. Hager

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2009 IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2009
Pages726-729
Number of pages4
DOIs
StatePublished - 2009
Event2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 - Boston, MA, United States
Duration: Jun 28 2009Jul 1 2009

Publication series

NameProceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009

Other

Other2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
CountryUnited States
CityBoston, MA
Period6/28/097/1/09

Keywords

  • Boosting
  • Image registration
  • Learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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