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Related Concept Videos

Endoscopic Procedures III: Video Capsule Endoscopy01:28

Endoscopic Procedures III: Video Capsule Endoscopy

Capsule endoscopy, or wireless or video capsule endoscopy, is a diagnostic procedure for examining the entire gastrointestinal tract. Patients swallow a capsule about the size of a vitamin tablet. The capsule is equipped with a transmitter, a battery, an LED light source, and a color video camera to capture images throughout the gastrointestinal tract. This procedure is particularly useful for diagnosing conditions such as Crohn's disease, ulcerative colitis, tumors, polyps, ulcers, unexplained...

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Capsule endoscopy image analysis using texture information from various colour models.

Vasileios S Charisis1, Leontios J Hadjileontiadis, Christos N Liatsos

  • 1Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, GR 54124 Thessaloniki, Greece.

Computer Methods and Programs in Biomedicine
|November 8, 2011
PubMed
Summary

This study introduces a new method for analyzing wireless capsule endoscopy (WCE) images to detect ulcers. The technique uses color-texture analysis to achieve over 95% accuracy in identifying intestinal ulcerations.

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Area of Science:

  • Medical Imaging
  • Gastroenterology
  • Computer Vision

Background:

  • Wireless capsule endoscopy (WCE) offers non-invasive visualization of the digestive tract, particularly the small bowel.
  • Physicians face challenges reviewing the large volume of WCE images.
  • Accurate detection of intestinal diseases like ulceration is crucial.

Purpose of the Study:

  • To develop an automated method for discriminating WCE images of intestinal ulcerations.
  • To investigate the utility of color-texture features for ulcer detection.

Main Methods:

  • Utilized wireless capsule endoscopy (WCE) images.
  • Employed bidimensional ensemble empirical mode decomposition for image pre-processing.
  • Applied differential lacunarity analysis on RGB, HSV, and CIE Lab color spaces to extract texture patterns.
  • Focused on color-texture features to differentiate healthy and ulcerous tissues.

Main Results:

  • Achieved promising classification performance with a mean accuracy exceeding 95%.
  • Demonstrated the effectiveness of color-texture analysis in identifying ulcerous regions.
  • Highlighted the potential for automatic analysis of WCE images.

Conclusions:

  • The proposed scheme shows high potential for automated WCE image analysis.
  • Color-texture feature analysis is a viable approach for detecting intestinal ulcerations.
  • This method can significantly aid physicians in diagnosing WCE-related findings.