ISSN: 0973-7510

E-ISSN: 2581-690X

M. Kanchana1 and P. Varalakshmi2
1 Research Scholar, Anna University, Chennai, India.
2Madras Institute of Technology, Anna University, Chennai, India.
J Pure Appl Microbiol. 2015;9(Spl. Edn. Aug.):117-125
© The Author(s). 2015
Received: 10/02/2015 | Accepted: 27/04/2015 | Published: 31/08/2015
Abstract

In this paper, a computer aided breast cancer diagnosis system using Non sub sampled Shearlet Transform (NSST) is proposed. A two-step approach is developed to classify the given mammogram. At first, the given mammogram is classified into abnormal or normal followed by the classification of abnormal severity into either malignant or benign using support vector machine classifier. The proposed approach uses shearlet moments to classify the digital mammograms. From the shearlet coefficients, up to 4th order moments are computed. The results shows that the method based on shearlet moments is very robust over other multiresolutional analysis such as wavelet and curvelet transform. Also, it presents an excellent classification rate of over 90% for all considered cases.

Keywords

Breast cancer, statistical moments, shearlet transform, wavelets, curvelets

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© The Author(s) 2015. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License which permits unrestricted use, sharing, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.