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Detection of Microcalcifications Using Coordinate Logic Filters and Artificial Neural Networks

  • J. Quintanilla-Domínguez
  • M. G. Cortina-Januchs
  • J. M. Barrón-Adame
  • A. Vega-Corona
  • F. S. Buendía-Buendía
  • D. Andina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5602)

Abstract

Breast cancer is one of the leading causes to women mortality in the world. Cluster of Microcalcifications (MCC) in mammograms can be an important early sign of breast cancer, the detection is important to prevent and treat the disease. In this paper, we present a novel method for the detection of MCC in mammograms which consists of image enhancement by histogram adaptive equalization technique, MCC edge detection by Coordinate Logic Filters (CLF), generation, clustering and labelling of suboptimal features vectors by means of Self Organizing Map (SOM) Neural Network. Like comparison we applied an unsupervised clustering K-means in the stage of labelling of our method. In the labelling stage, we obtain better results with the proposed SOM Neural Network compared with the k-means algorithm. Then, we show that the proposed method can locate MCCs in an efficient way.

Keywords

Image Enhancement Digital Mammogram Digital Signal Processing Application Image Enhancement Technique Mammographic Image Analysis Society 
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

  • J. Quintanilla-Domínguez
    • 1
  • M. G. Cortina-Januchs
    • 1
  • J. M. Barrón-Adame
    • 2
  • A. Vega-Corona
    • 2
  • F. S. Buendía-Buendía
    • 1
  • D. Andina
    • 1
  1. 1.Group for Automation in Signals and CommunicationsUniversidad Politécnica de MadridSpain
  2. 2.Laboratorio de Inteligencia ComputacionalUniversidad de GuanajuatoMéxico

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