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Meaningful Features for Computerized Detection of Breast Cancer

  • José Anibal Arias
  • Verónica Rodríguez
  • Rosebet Miranda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

After pre-processing and segmenting suspicious masses in mammographies based on the Top-Hat and Markov Random Fields methods, we developed a mass-detection algorithm that uses gray level co-occurrence matrices, gray level difference statistics, gray level run length statistics, shape descriptors and intensity parameters as the entry of a vector support machine classifier. During the classification process we test up to 63 image features, keeping the 35 most important and obtaining 85% of accuracy score.

Keywords

Breast cancer CADx Image features SVM 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • José Anibal Arias
    • 1
  • Verónica Rodríguez
    • 1
  • Rosebet Miranda
    • 1
  1. 1.Universidad Tecnológica de la MixtecaHuajuapan de LeónMéxico

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