Contourlet-based mammography mass classification

  • Authors:

  • Fatemeh Moayedi;Zohreh Azimifar;Reza Boostani;Serajodin Katebi

  • Affiliations:

  • Computer Science & Engineering Department, Shiraz University, Shiraz, Iran;Computer Science & Engineering Department, Shiraz University, Shiraz, Iran;Computer Science & Engineering Department, Shiraz University, Shiraz, Iran;Computer Science & Engineering Department, Shiraz University, Shiraz, Iran

  • Venue:
  • ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
  • Year:
  • 2007

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Abstract

The research presented in this paper is aimed at the development of an automatic mass classification of mammograms. This paper focuses on using contourlet-based multi-resolution texture analysis. The contourlet transform is a new two-dimensional extension of the wavelet transform using multi-scale framework as well as directional filter banks. The proposed method consists of three steps: removing pectoral muscle and segmenting regions of interest, extracting the most discriminative texture features based on the contourlet coefficients, and finally creating a classifier, which identifies various tissues. In this research classification is performed based on the idea of Successive Enhancement Learning (SEL) weighted Support Vector Machine (SVM). The main contribution of this work is exploiting the superiority of the contourlets to the-state-of-the-art multi-scale techniques. Experimental results show that contourlet-based feature extraction in conjunction with the SEL weighted SVM classifier significantly improves breast mass detection.