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Local Polynomial Approximation for Unsupervised Segmentation of Endoscopic Images

  • Artur Klepaczko
  • Piotr Szczypiński
  • Piotr Daniel
  • Marek Pazurek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6375)

Abstract

In this paper we present a novel technique for unsupervised texture segmentation of wireless capsule endoscopic images of the human gastrointestinal tract. Our approach integrates local polynomial approximation algorithm with the well-founded methods of color texture analysis and clustering (k-means) leading to a robust segmentation procedure which produces fine-grained segments well matched to the image contents.

Keywords

Endoscopic Image Capsule Endoscopy Video Wireless Capsule Endoscopy Unsupervised Segmentation Local Polynomial Approximation 
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

  • Artur Klepaczko
    • 1
  • Piotr Szczypiński
    • 1
  • Piotr Daniel
    • 2
  • Marek Pazurek
    • 2
  1. 1.Institute of ElectronicsTechnical University of ŁódźWólczańska
  2. 2.Department of Digestive Tract DiseaseMedical University of ŁódźKopcińskiego

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