Assessment of crohn's disease lesions in wireless capsule endoscopy images

Rajesh Kumar, Qian Zhao, Sharmishtaa Seshamani, Gerard Mullin, Gregory Hager, Themistocles Dassopoulos

Research output: Contribution to journalArticlepeer-review

51 Scopus citations


Capsule endoscopy (CE) provides noninvasive access to a large part of the small bowel that is otherwise inaccessible without invasive and traumatic treatment. However, it also produces large amounts of data (approximately 50000 images) that must be then manually reviewed by a clinician. Such large datasets provide an opportunity for application of image analysis and supervised learning methods. Automated analysis of CE images has only focused on detection, and often only for bleeding. Compared to these detection approaches, we explored assessment of discrete disease for lesions created by mucosal inflammation in Crohns disease (CD). Our work is the first study to systematically explore supervised classification for CD lesions, a classifier cascade to classify discrete lesions, as well as quantitative assessment of lesion severity. We used a well-developed database of 47 studies for evaluation of these methods. The developed methods show high agreement with ground truth severity ratings manually assigned by an expert, and good precision (90 for lesion detection) and recall (90) for lesions of varying severity.

Original languageEnglish (US)
Article number6051474
Pages (from-to)355-362
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Issue number2
StatePublished - Feb 2012


  • Content-based image retrieval
  • Crohns disease
  • statistical classification
  • wireless capsule endoscopy (CE)

ASJC Scopus subject areas

  • Biomedical Engineering


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