Robustness of a CAD system on digitized mammograms

  • Authors:

  • Antonio García-Manso;Carlos J. García-Orellana;Ramón Gallardo-Caballero;Nico Lanconelli;Horacio González-Velasco;Miguel Macías-Macías

  • Affiliations:

  • Pattern Classification and Image Analysis Group (CAPI), University of Extremadura, Badajoz, Extremadura, Spain;Pattern Classification and Image Analysis Group (CAPI), University of Extremadura, Badajoz, Extremadura, Spain;Pattern Classification and Image Analysis Group (CAPI), University of Extremadura, Badajoz, Extremadura, Spain;Medical Imaging Group, Physics Dpt., Bologna University, Bologna, Italy;Pattern Classification and Image Analysis Group (CAPI), University of Extremadura, Badajoz, Extremadura, Spain;Pattern Classification and Image Analysis Group (CAPI), University of Extremadura, Badajoz, Extremadura, Spain

  • Venue:
  • ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
  • Year:
  • 2012

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Abstract

In this paper we study the robustness of our CAD system, since this is one of the main factors that determine its quality. A CAD system must guarantee consistent performance over time and in various clinical situations. Our CAD system is based on the extraction of features from the mammographic image by means of Independent Component Analysis, and machine learning classifiers, such as Neural Networks and Support Vector Machine. To measure the robustness of our CAD system we have used the digitized mammograms of the USF's DDSM database, because this database was built by digitizing mammograms from four different institutions (four different scanner) during more than 10 years. Thus, we can use the mammograms digitized with one scanner to train the system and the remaining to evaluate the performance, what gives us a measure of the robustness of our CAD system.