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Author (up) Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; C. Malagelada; Petia Radeva edit   pdf
doi  openurl
  Title A Machine Learning framework using SOMs: Applications in the Intestinal Motility Assessment Type Book Chapter
  Year 2006 Publication 11th Iberoamerican Congress on Pattern Recognition Abbreviated Journal  
  Volume 4225 Issue Pages 188–197  
  Keywords  
  Abstract 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.  
  Address Cancun (Mexico)  
  Corporate Author Thesis  
  Publisher Springer Verlag Place of Publication Berlin-Heidelberg Editor J.P. Martinez–Trinidad et al  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area 800 Expedition Conference CIARP06  
  Notes MV;OR;MILAB;SIAI Approved no  
  Call Number BCNPCL @ bcnpcl @ VSV2006d; IAM @ iam @ VSV2006e Serial 729  
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