A Machine Learning Framework Using SOMs: Applications in the Intestinal Motility Assessment

  • Fernando Vilariño
  • Panagiota Spyridonos
  • Jordi Vitrià
  • Carolina Malagelada
  • Petia Radeva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)


Small Bowel Motility Assessment by means of Wireless Capsule Video Endoscopy constitutes a novel clinical methodology in which a capsule with a micro-camera attached to it is swallowed by the patient, emitting a RF signal which is recorded as a video of its trip throughout the gut. In order to overcome the main drawbacks associated with this technique -mainly related to the large amount of visualization time required-, our efforts have been focused on the development of a machine learning system, built up in sequential stages, which provides the specialists with the useful part of the video, rejecting those parts not valid for analysis. We successfully used Self Organized Maps in a general semi-supervised framework with the aim of tackling the different learning stages of our system. The analysis of the diverse types of images and the automatic detection of intestinal contractions is performed under the perspective of intestinal motility assessment in a clinical environment.


Support Vector Machine Capsule Endoscopy Intestinal Motility Wireless Capsule Endoscopy Motility Assessment 
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 2006

Authors and Affiliations

  • Fernando Vilariño
    • 1
  • Panagiota Spyridonos
    • 1
  • Jordi Vitrià
    • 1
  • Carolina Malagelada
    • 2
  • Petia Radeva
    • 1
  1. 1.Computer Vision CenterUniversitat Autònoma de BarcelonaBellaterraSpain
  2. 2.Hospital Vall d’HebronBarcelonaSpain

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