Comparison of PCA and ANOVA for Information Selection of CC and MLO Views in Classification of Mammograms

  • Ricardo de Souza Jacomini
  • Marcelo Zanchetta do Nascimento
  • Rogério Daniel Dantas
  • Rodrigo Pereira Ramos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7435)

Abstract

In this paper, we present a method for extraction and attribute selection for textural features classification using the fusion of information from the mediolateral oblique (MLO) view and craniocaudal (CC) views. In the extraction step, wavelet coefficients together with singular value decomposition technique were applied to reduce the number of textural attributes. For the selection stage and reduction of attributes, an evaluation of the Analysis of Variance (ANOVA) technique and Principal Component Analysis (PCA) is performed when used for textural information reduction. In the final step, it was used the Random Forest algorithm for classifying regions of interest (ROIs) of the set of images determined as normal, benign and malignant. The experiments showed that ANOVA reached the higher proportional attributes reduction and featured the best results for information fusion of CC and MLO views. The best classification rates were obtained with ANOVA for normal-benign images (area under the receiver operating characteristic curve - AUC = 0.78) and benign-malignant images (AUC = 0.83) and with the PCA method for normal-malignant images (AUC = 0.85).

Keywords

Singular Value Decomposition Discrete Wavelet Transform Mammographic Image Digital Mammogram Random Forest Algorithm 
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 2012

Authors and Affiliations

  • Ricardo de Souza Jacomini
    • 1
  • Marcelo Zanchetta do Nascimento
    • 1
  • Rogério Daniel Dantas
    • 1
  • Rodrigo Pereira Ramos
    • 2
  1. 1.Centro de Matemática, Computação e CogniçãoUniversidade Federal do ABC (UFABC)Santo AndréBrasil
  2. 2.Colegiado de Engenharia ElétricaUniversidade Federal do Vale do São Francisco (UNIVASF)JuazeiroBrasil

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