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.
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Moayedi, F., Azimifar, Z., Boostani, R., Katebi, S. (2007). Contourlet-Based Mammography Mass Classification. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2007. Lecture Notes in Computer Science, vol 4633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74260-9_82
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DOI: https://doi.org/10.1007/978-3-540-74260-9_82
Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-540-74260-9
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