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A Novel Approach for Detecting and Classifying Breast Cancer in Mammogram Images

A Novel Approach for Detecting and Classifying Breast Cancer in Mammogram Images

S. Shanthi, V. Murali Bhaskaran
Copyright: © 2013 |Volume: 9 |Issue: 1 |Pages: 19
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781466631434|DOI: 10.4018/jiit.2013010102
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MLA

Shanthi, S., and V. Murali Bhaskaran. "A Novel Approach for Detecting and Classifying Breast Cancer in Mammogram Images." IJIIT vol.9, no.1 2013: pp.21-39. http://doi.org/10.4018/jiit.2013010102

APA

Shanthi, S. & Bhaskaran, V. M. (2013). A Novel Approach for Detecting and Classifying Breast Cancer in Mammogram Images. International Journal of Intelligent Information Technologies (IJIIT), 9(1), 21-39. http://doi.org/10.4018/jiit.2013010102

Chicago

Shanthi, S., and V. Murali Bhaskaran. "A Novel Approach for Detecting and Classifying Breast Cancer in Mammogram Images," International Journal of Intelligent Information Technologies (IJIIT) 9, no.1: 21-39. http://doi.org/10.4018/jiit.2013010102

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Abstract

This study uses data mining techniques for computer-aided diagnosis that involves the feature extraction for cancer detection, so as to help doctors towards making optimal decisions quickly and accurately. Features play an important role in detecting the cancer in the digital mammogram and feature extraction stage is the most vital and difficult stage. In this paper, an enhanced feature extraction method named Multiscale Surrounding Region Dependence Method (MSRDM) is proposed to be effective in classifying the mammogram images into normal or benign or malignant. This proposed system is based on a four-step procedure: Regions of Interest specification, two dimensional discrete wavelet transformation, and multiscale surrounding region dependence matrix computation and feature extraction. The performance of the proposed feature set is compared with the conventional texture-analysis methods such as gray level cooccurence matrix features and surrounding region dependence method features. Experiments have been conducted on both real and benchmark data and the results have been proved to be progressive.

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