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Detection of Microcalcifications in Mammograms by the Combination of a Neural Detector and Multiscale Feature Enhancement

  • Diego Andina
  • Antonio Vega-Corona
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2085)

Abstract

We propose a two steps method for the automatic classifi- cation of microcalcifications in Mammograms. The first step performs the improvement of the visualization of any abnormal lesion through feature enhancement based in multiscale wavelet representations of the mammographic images. In a second step the automatic recognition of microcalcifications is achieved by the application of a Neural Network optimized in the Neyman-Pearson sense. That means that the Neural Network presents a controlled and very low probability of classifying abnormal images as normal.

Keywords

False Alarm Probability Edge Curve Mammographic Image Abnormal Mammogram Dyadic Wavelet 
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 2001

Authors and Affiliations

  • Diego Andina
    • 1
  • Antonio Vega-Corona
    • 2
  1. 1.Departamento de Señales, Sistemas y Radiocomunicaciones, E.T.S.I. TelecomunicaciónUniversidad Politécnica de MadridSpain
  2. 2.F.I.M.E.EUniversidad de GuanajuatoGuanajuatoMexico

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