Abnormal image detection in endoscopy videos using a filter bank and local binary patterns

Ruwan Nawarathna, Jung Hwan Oh, Jayantha Muthukudage, Wallapak Tavanapong, Johnny Wong, Piet C. de Groen, Shou Jiang Tang

Research output: Contribution to journalArticlepeer-review

58 Scopus citations

Abstract

Finding mucosal abnormalities (e.g., erythema, blood, ulcer, erosion, and polyp) is one of the most essential tasks during endoscopy video review. Since these abnormalities typically appear in a small number of frames (around 5% of the total frame number), automated detection of frames with an abnormality can save physician[U+05F3]s time significantly. In this paper, we propose a new multi-texture analysis method that effectively discerns images showing mucosal abnormalities from the ones without any abnormality since most abnormalities in endoscopy images have textures that are clearly distinguishable from normal textures using an advanced image texture analysis method. The method uses a "texton histogram" of an image block as features. The histogram captures the distribution of different "textons" representing various textures in an endoscopy image. The textons are representative response vectors of an application of a combination of Leung and Malik (LM) filter bank (i.e., a set of image filters) and a set of Local Binary Patterns on the image. Our experimental results indicate that the proposed method achieves 92% recall and 91.8% specificity on wireless capsule endoscopy (WCE) images and 91% recall and 90.8% specificity on colonoscopy images.

Original languageEnglish (US)
Pages (from-to)70-91
Number of pages22
JournalNeurocomputing
Volume144
DOIs
StatePublished - Nov 20 2014

Keywords

  • Colonoscopy
  • Filter bank
  • Local binary pattern
  • Texton
  • Texton dictionary
  • Wireless capsule endoscopy

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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