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Automatic Recognition of Microcalcifications in Mammography Images through Fractal Texture Analysis

  • Hernán Darío Vargas Cardona
  • Álvaro Orozco
  • Mauricio A. Álvarez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8888)

Abstract

Mammography images are widely used for detection of microcalcifications (MCs), which constitute the early stage of breast cancer. Moreover, these images allow the medical specialist to perform a timely diagnosis and to prevent complications in patients. Automatic identification of MCs in mammography images may be useful as a decision support given by a specialist. In this paper, we construct a mammography image database with medical validation and expert labeling. The test subjects are from a local population located in the Eje cafetero, Colombia. Also, we present a methodology for automatic recognition of microcalcifications based on segmentation with fractal texture analysis (SFTA) and a support vector machine (SVM). For a comparison framework with the state of the art, we compare our methodology with the local binary patterns (LBP) method, that is widely applied in digital images processing. Results show that SFTA methodology for recognition of MCs achieves an accuracy over 92.5% improving significatively when compared to LBP. Also, our database satisfies the epidemiological parameters to represent a local population.

Keywords

Support Vector Machine Local Binary Pattern Digital Mammography Automatic Recognition Mammography Image 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Hernán Darío Vargas Cardona
    • 1
  • Álvaro Orozco
    • 1
  • Mauricio A. Álvarez
    • 1
  1. 1.Department of Electric EngineeringUniversidad Tecnológica de PereiraPereiraColombia

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