Learning disease severity for capsule endoscopy images

  • Authors:

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

  • Affiliations:

  • Department of Computer Science, Johns Hopkins University, Baltimore, MD and Johns Hopkins Hospital, Baltimore, MD and Washington University, School of Medicine St. Louis, MO;Department of Computer Science, Johns Hopkins University, Baltimore, MD and Johns Hopkins Hospital, Baltimore, MD and Washington University, School of Medicine St. Louis, MO;Department of Computer Science, Johns Hopkins University, Baltimore, MD and Johns Hopkins Hospital, Baltimore, MD and Washington University, School of Medicine St. Louis, MO;Department of Computer Science, Johns Hopkins University, Baltimore, MD and Johns Hopkins Hospital, Baltimore, MD and Washington University, School of Medicine St. Louis, MO;Department of Computer Science, Johns Hopkins University, Baltimore, MD and Johns Hopkins Hospital, Baltimore, MD and Washington University, School of Medicine St. Louis, MO;Department of Computer Science, Johns Hopkins University, Baltimore, MD and Johns Hopkins Hospital, Baltimore, MD and Washington University, School of Medicine St. Louis, MO;Department of Computer Science, Johns Hopkins University, Baltimore, MD and Johns Hopkins Hospital, 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

Quantified Score

Hi-index 0.00

Visualization

Abstract

Wireless capsule endoscopy (CE) is increasing being used to assess several gastrointestinal(GI) diseases and disorders. Current clinical methods are based on subjective evaluation of images. In this paper, we develop a method for ranking lesions appearing in CE images. This ranking is based on pairwise comparisons among representative images supplied by an expert. With such sparse pairwise rank information for a small number of images, we investigate methods for creating and evaluating global ranking functions. In experiments with CE images, we train statistical classifiers using color and edge feature descriptors extracted frommanually annotated regions of interest. Experiments on a data set using Crohn's disease lesions for lesion severity are presented with the developed ranking functions achieve high accuracy rates.