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Automated Recognition of Abnormal Structures in WCE Images Based on Texture Most Discriminative Descriptors

  • Piotr Szczypiński
  • Artur Klepaczko
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 84)

Summary

In this paper we study the problem of classification of wireless capsule endoscopy images (WCE). We aim at developing a computer system that would aid in medical diagnosis procedure. The goal is to automatically detect images showing pathological alterations in an 8-hour-long WCE video. We focus on three classes of pathologies, ulcers, bleedings and petechia, since they are typical for several diseases of intestines. Utilized are methods for image texture and color analysis to obtain numerical description of images. Then, three methods for selection of most discriminative descriptors are used, namely Vector Supported Convex Hull, Support Vector Machines and Radial Basis Function Networks. The results produced by the three methods are compared.

Keywords

Support Vector Machine Convex Hull Feature Subset Radial Basis Function Network Capsule Endoscopy Video 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Piotr Szczypiński
    • 1
  • Artur Klepaczko
    • 1
  1. 1.Institute of ElectronicsTechnical University of ŁodzŁodz

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