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

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Intelligent Data Engineering and Automated Learning - IDEAL 2012 (IDEAL 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7435))

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  • 9 Citations

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).

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Author information

Authors and Affiliations

  1. Centro de Matemática, Computação e Cognição, Universidade Federal do ABC (UFABC), Santo André, SP, Brasil

    Ricardo de Souza Jacomini, Marcelo Zanchetta do Nascimento & Rogério Daniel Dantas

  2. Colegiado de Engenharia Elétrica, Universidade Federal do Vale do São Francisco (UNIVASF), Juazeiro, BA, Brasil

    Rodrigo Pereira Ramos

Authors
  1. Ricardo de Souza Jacomini
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  2. Marcelo Zanchetta do Nascimento
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  3. Rogério Daniel Dantas
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  4. Rodrigo Pereira Ramos
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, The University of Manchester, M13 9PL, Manchester, UK

    Hujun Yin

  2. Department of Electrical Engineering, Federal University of Rio Grande do Norte, Lagoa Nova, 59072-970, Natal, RN, Brazil

    José A. F. Costa

  3. Department of Teleinformatics Engineering, Federal University of Ceará, Campus of Pici, CP 6005, 60455-760, Fortaleza, CE, Brazil

    Guilherme Barreto

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© 2012 Springer-Verlag Berlin Heidelberg

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de Souza Jacomini, R., do Nascimento, M.Z., Dantas, R.D., Ramos, R.P. (2012). Comparison of PCA and ANOVA for Information Selection of CC and MLO Views in Classification of Mammograms. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_15

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  • DOI: https://doi.org/10.1007/978-3-642-32639-4_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32638-7

  • Online ISBN: 978-3-642-32639-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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