Part-based multiderivative edge cross-sectional profiles for polyp detection in colonoscopy

Yi Wang, Wallapak Tavanapong, Johnny Wong, Jung Hwan Oh, Piet C. De Groen

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


This paper presents a novel technique for automated detection of protruding polyps in colonoscopy images using edge cross-section profiles (ECSP). We propose a part-based multiderivative ECSP that computes derivative functions of an edge cross-section profile and segments each of these profiles into parts. Therefore, we can model or extract features suitable for each part. Our features obtained from the parts can effectively describe complex properties of protruding polyps including the shape of the parts, texture, and protrusion and smoothness of the polyp surface. We evaluated our method against two existing polyp image detection techniques on 42 different polyps, including those with little protrusion. Each polyp has a large variation of appearance in viewing angles, light conditions, and scales in different images. The evaluation showed that our technique outperformed the existing techniques in both accuracy and analysis time. Our method has a higher area under the free-response receiver operating characteristic curve. For instance, when both techniques have a true positive rate for polyp image detection of 81.4%, the average number of false regions per image of our technique is 0.32 compared to 1.8 of the best existing technique under study. Additionally, our technique can precisely mark edges of candidate polyp regions as visual feedback. These results altogether indicate that our technique is promising to provide visual feedback of polyp regions in clinical practice.

Original languageEnglish (US)
Article number6626652
Pages (from-to)1379-1389
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Issue number4
StatePublished - Jul 2014


  • Colonoscopy
  • edge cross-section profile (ECSP)
  • medical imaging analysis
  • polyp detection

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics
  • Electrical and Electronic Engineering
  • Health Information Management


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