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Classification of breast masses via nonlinear transformation of features based on a kernel matrix

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Abstract

We propose methods to perform a certain nonlinear transformation of features based on a kernel matrix, before the classification step, aiming to improve the discriminating power of the comparatively weak edge-sharpness and texture features of breast masses in mammograms, and seek better incorporation of features representing different radiological characteristics than shape features only. Kernel principal component analysis (KPCA) is applied to improve the discriminating power of each single feature in an expanded feature space and the discriminating capability of different feature combinations in other transformed, more informative, lower-dimensional feature spaces. A kernel partial least squares (KPLS) method is developed to derive score vectors for a shape feature set, and an edge-sharpness and texture feature set, respectively, with minimal covariance between each other, to help in achieving improved diagnosis using multiple radiological characteristics of breast masses. Fisher’s linear discriminant analysis (FLDA) is employed to evaluate the classification capability of the transformed features. The methods were tested with a set of 57 regions in mammograms, of which 20 are related to malignant tumors and 37 to benign masses, represented using five shape features, three edge-sharpness features, and 14 texture features. The classification performance of the edge-sharpness and texture features, via KPCA transformation, was significantly improved from 0.75 to 0.85 in terms of the area under the receiver operating characteristics curve (A z ). The classification performance of all of the shape, edge-sharpness, and texture features, via KPLS transformation, was improved from 0.95 to 1.0 in A z value.

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Acknowledgment

T. Mu would like to acknowledge financial support from the Overseas Research Students Awards Scheme (ORSAS), UK; the Hsiang Su Coppin Memorial Scholarship Fund, and the University of Liverpool, UK. We thank the University Research Grants Committee of the University of Calgary, Canada, and the Medical Research Council (the Interdisciplinary Bridging Awards), UK, for financial support.

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Authors and Affiliations

  1. Department of Electrical Engineering and Electronics, The University of Liverpool, Liverpool, Brownlow Hill, L69 3GJ, UK

    Tingting Mu & Asoke K. Nandi

  2. Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada, T2N 1N4

    Rangaraj M. Rangayyan

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  1. Tingting Mu
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  2. Asoke K. Nandi
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  3. Rangaraj M. Rangayyan
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Correspondence to Asoke K. Nandi.

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Mu, T., Nandi, A.K. & Rangayyan, R.M. Classification of breast masses via nonlinear transformation of features based on a kernel matrix. Med Bio Eng Comput 45, 769–780 (2007). https://doi.org/10.1007/s11517-007-0211-0

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  • DOI: https://doi.org/10.1007/s11517-007-0211-0

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