Cascade analysis for intestinal contraction detection

F. Vilariñ, P. Spyridonous, J. Vitria, F. Azpiroz, P. Radeva

Research output: Contribution to journalArticleResearchpeer-review

8 Citations (Scopus)

Abstract

In this work, we address the study of intestinal contractions in a novel approach based on a machine learning framework to process data from Wireless Capsule Video Endoscopy Wireless endoscopy represents a unique way to visualize the intestine motility by creating long videos to visualize intestine dynamics. In this paper we argue that to analyze huge amount of wireless endoscopy data and define robust methods for contraction detection we should base our approach on sophisticated machine learning techniques. In particular, we propose a cascade of classifiers in order to remove different physiological phenomenon and obtain the motility pattern of small intestines. Our results show obtaining high specificity and sensitivity rates that highlight the high efficiency of the selected approach and support the feasibility of the proposed methodology in the automatic detection and analysis of intestine contractions.
Original languageEnglish
Pages (from-to)9-10
JournalInternational journal of computer assisted radiology and surgery
Volume1
Issue numberSUPPL. 7
DOIs
Publication statusPublished - 1 Jan 2006

Keywords

  • Anisotropic features
  • Cascade of classifiers
  • Intestine video analysis
  • Support vector machine

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