Categorization and segmentation of intestinal content frames for wireless capsule endoscopy

Santí Segui, Michal Drozdzal, Fernando Vilarino, Carolina Malagelada, Fernando Azpiroz, Petia Radeva, Jordi Vitria

Research output: Contribution to journalArticleResearchpeer-review

32 Citations (Scopus)


Wireless capsule endoscopy (WCE) is a device that allows the direct visualization of gastrointestinal tract with minimal discomfort for the patient, but at the price of a large amount of time for screening. In order to reduce this time, several works have proposed to automatically remove all the frames showing intestinal content. These methods label frames as {intestinal content-clear} without discriminating between types of content (with different physiological meaning) or the portion of image covered. In addition, since the presence of intestinal content has been identified as an indicator of intestinal motility, its accurate quantification can show a potential clinical relevance. In this paper, we present a method for the robust detection and segmentation of intestinal content in WCE images, together with its further discrimination between turbid liquid and bubbles. Our proposal is based on a twofold system. First, frames presenting intestinal content are detected by a support vector machine classifier using color and textural information. Second, intestinal content frames are segmented into {turbid, bubbles, and clear} regions. We show a detailed validation using a large dataset. Our system outperforms previous methods and, for the first time, discriminates between turbid from bubbles media. © 2012 IEEE.
Original languageEnglish
Article number6319409
Pages (from-to)1341-1352
JournalIEEE Transactions on Information Technology in Biomedicine
Issue number6
Publication statusPublished - 18 Dec 2012


  • Image segmentation
  • informative frames
  • intestinal content
  • machine learning
  • wireless capsule endoscopy (WCE)


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