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Microcalcifications Detection Using PFCM and ANN

  • A. Vega-Corona
  • J. Quintanilla-Domínguez
  • B. Ojeda-Magaña
  • M. G. Cortina-Januchs
  • A. Marcano-Cedeño
  • R. Ruelas
  • D. Andina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6718)

Abstract

This work presents a method to detect Microcalcifications in Regions of Interest from digitized mammograms. The method is based mainly on the combination of Image Processing, Pattern Recognition and Artificial Intelligence. The Top-Hat transform is a technique based on mathematical morphology operations that, in this work is used to perform contrast enhancement of microcalcifications in the region of interest. In order to find more or less homogeneous regions in the image, we apply a novel image sub-segmentation technique based on Possibilistic Fuzzy c-Means clustering algorithm. From the original region of interest we extract two window-based features, Mean and Deviation Standard, which will be used in a classifier based on a Artificial Neural Network in order to identify microcalcifications. Our results show that the proposed method is a good alternative in the stage of microcalcifications detection, because this stage is an important part of the early Breast Cancer detection.

Keywords

Microcalcifications detection and classification Top-Hat transform Possibilistic Fuzzy c-Means Artificial Neural Networks 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • A. Vega-Corona
    • 2
  • J. Quintanilla-Domínguez
    • 1
    • 3
  • B. Ojeda-Magaña
    • 1
    • 2
  • M. G. Cortina-Januchs
    • 1
    • 3
  • A. Marcano-Cedeño
    • 1
  • R. Ruelas
    • 2
  • D. Andina
    • 1
  1. 1.Group for Automation in Signals and Communications GASCTechnical University of MadridMadridSpain
  2. 2.Computational Intelligence Laboratory LABINCO-DICISUniversity of GuanajuatoSalamancaMexico
  3. 3.Department of Projects Engineering DIP-CUCEIUniversity of Guadalajara.ZapopanMexico

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