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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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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Abnormal pattern detection in Wireless Capsule Endoscopy images using nonlinear analysis in RGB color space.

Vasileios Charisis1, Leontios J Hadjileontiadis, Christos N Liatsos

  • 1Electrical and Computer Engineering Department, Aristotle University of Thessaloniki, Greece. vcharisis@ee.auth.gr

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
Summary

Wireless Capsule Endoscopy (WCE) generates many images. A new method uses Bidimensional Ensemble Empirical Mode Decomposition and lacunarity analysis to accurately detect ulcers in the gastrointestinal tract from WCE images.

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Area of Science:

  • Medical Imaging
  • Gastroenterology
  • Computer Vision

Background:

  • Wireless Capsule Endoscopy (WCE) allows non-invasive gastrointestinal tract visualization.
  • Reviewing extensive WCE image data is time-consuming.
  • Ulcers are a common gastrointestinal disease requiring accurate detection.

Purpose of the Study:

  • To develop a novel technique for distinguishing pathogenic endoscopic images related to gastrointestinal ulcers.
  • To automate the analysis of WCE images for ulcer detection.

Main Methods:

  • Applied Bidimensional Ensemble Empirical Mode Decomposition to WCE RGB images to extract Intrinsic Mode Functions (IMFs).
  • Utilized lacunarity analysis to quantify texture patterns for discriminating ulcer regions from normal mucosa.
  • Employed a classification approach to differentiate abnormal from normal WCE images.

Main Results:

  • The proposed method successfully extracted structural differences using IMFs across various scales.
  • Lacunarity analysis effectively quantified texture variations between ulcerous and normal tissues.
  • Achieved promising classification accuracy exceeding 95% in distinguishing ulcer images.

Conclusions:

  • The developed technique shows high potential for automated WCE image analysis.
  • This approach can significantly reduce the time required for reviewing WCE data.
  • Offers a promising tool for improving the diagnosis of gastrointestinal ulcers.