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Pattern Analysis of Dermoscopic Images Based on FSCM Color Markov Random Fields

  • Carlos S. Mendoza
  • Carmen Serrano
  • Begoña Acha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5807)

Abstract

In this paper a method for pattern analysis in dermoscopic images of abnormally pigmented skin (melanocytic lesions) is presented. In order to diagnose a possible skin cancer, physicians assess the lesion according to different rules. The new trend in Dermatology is to classify the lesion by means of pattern irregularity. In order to analyze the pattern turbulence, lesions ought to be segmented into single pattern regions. Our classification method, when applied on overlapping lesion patches, provides a pattern chart that could ultimately allow for in-region single-texture turbulence analysis. Due to the color-textured appearance of these patterns, we present a novel method based on a Finite Symmetric Conditional Model (FSCM) Markov Random Field (MRF) color extension for the characterization and discrimination of pattern samples. Our classification success rate rises to 86%.

Keywords

Color Space Markov Random Field Healthy Skin Markov Random Melanocytic Lesion 
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 2009

Authors and Affiliations

  • Carlos S. Mendoza
    • 1
  • Carmen Serrano
    • 1
  • Begoña Acha
    • 1
  1. 1.Universidad of SevillaSevillaSpain

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