Microcalcifications detection using PFCM and ANN

  • Authors:

  • A. Vega-Corona;J. Quintanilla-Domínguez;B. Ojeda-Magaña;M. G. Cortina-Januchs;A. Marcano-Cedeño;R. Ruelas;D. Andina

  • Affiliations:

  • Computational Intelligence Laboratory LABINCO-DICIS, University of Guanajuato, Salamanca Guanajuato, Mexico;Group for Automation in Signals and Communications, Technical University of Madrid, Madrid, Spain and Department of Projects Engineering DIP-CUCEI, University of Guadalajara. Zapopan Jalisco, Mexi ...;Group for Automation in Signals and Communications, Technical University of Madrid, Madrid, Spain and Computational Intelligence Laboratory LABINCO-DICIS, University of Guanajuato, Salamanca Guana ...;Group for Automation in Signals and Communications, Technical University of Madrid, Madrid, Spain and Department of Projects Engineering DIP-CUCEI, University of Guadalajara. Zapopan Jalisco, Mexi ...;Group for Automation in Signals and Communications, Technical University of Madrid, Madrid, Spain;Computational Intelligence Laboratory LABINCO-DICIS, University of Guanajuato, Salamanca Guanajuato, Mexico;Group for Automation in Signals and Communications, Technical University of Madrid, Madrid, Spain

  • Venue:
  • MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
  • Year:
  • 2011

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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.