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Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions

  • Panagiota Spyridonos
  • Fernando Vilariño
  • Jordi Vitrià
  • Fernando Azpiroz
  • Petia Radeva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

Abstract

Wireless endoscopy is a very recent and at the same time unique technique allowing to visualize and study the occurrence of contractions and to analyze the intestine motility. Feature extraction is essential for getting efficient patterns to detect contractions in wireless video endoscopy of small intestine. We propose a novel method based on anisotropic image filtering and efficient statistical classification of contraction features. In particular, we apply the image gradient tensor for mining informative skeletons from the original image and a sequence of descriptors for capturing the characteristic pattern of contractions. Features extracted from the endoluminal images were evaluated in terms of their discriminatory ability in correct classifying images as either belonging to contractions or not. Classification was performed by means of a support vector machine classifier with a radial basis function kernel. Our classification rates gave sensitivity of the order of 90.84% and specificity of the order of 94.43% respectively. These preliminary results highlight the high efficiency of the selected descriptors and support the feasibility of the proposed method in assisting the automatic detection and analysis of contractions.

Keywords

Support Vector Machine Capsule Endoscopy Intestinal Lumen Structural Tensor Radial Basis Function Kernel 
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 2006

Authors and Affiliations

  • Panagiota Spyridonos
    • 1
  • Fernando Vilariño
    • 1
  • Jordi Vitrià
    • 1
  • Fernando Azpiroz
    • 2
  • Petia Radeva
    • 1
  1. 1.Computer Vision Center and Computer Science Dept.Universitat Autonoma de BarcelonaBarcelonaSpain
  2. 2.Hospital Universitari Vall d’HebronBarcelonaSpain

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