Automatic Region-of-Interest Segmentation and Pathology Detection in Magnetically Guided Capsule Endoscopy

  • Philip W. Mewes
  • Dominik Neumann
  • Oleg Licegevic
  • Johannes Simon
  • Aleksandar Lj. Juloski
  • Elli Angelopoulou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

Magnetically-guided capsule endoscopy (MGCE) was introduced in 2010 as a procedure where a capsule in the stomach is navigated via an external magnetic field. The quality of the examination depends on the operator’s ability to detect aspects of interest in real time. We present a novel two step computer-assisted diagnostic-procedure (CADP) algorithm for indicating gastritis and gastrointestinal bleedings in the stomach during the examination. First, we identify and exclude subregions of bubbles which can interfere with further processing. Then we address the challenge of lesion localization in an environment with changing contrast and lighting conditions. After a contrast-normalized filtering, feature extraction is performed. The proposed algorithm was tested on 300 images of different patients with uniformly distributed occurrences of the target pathologies. We correctly segmented 84.72% of bubble areas. A mean detection rate of 86% for the target pathologies was achieved during a 5-fold leave-one-out cross-validation.

Keywords

Capsule Endoscopy Image Patch Capsule Endoscopy Video Wireless Capsule Endoscopy Intestinal Juice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Philip W. Mewes
    • 1
    • 2
  • Dominik Neumann
    • 1
  • Oleg Licegevic
    • 1
  • Johannes Simon
    • 1
  • Aleksandar Lj. Juloski
    • 1
  • Elli Angelopoulou
    • 2
  1. 1.Healthcare SectorSiemens AGErlangenGermany
  2. 2.Pattern Recognition LabUniversity of Erlangen-NürnbergErlangenGermany

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