Breast Cancer Diagnosis using Curvelet and Regional Features

Abstract

Mammography is the most customarily used prior procedures to diagnose breast cancer. The preprocessing step is essential to improve the contrast in the mammogram image, in this paper top-hat and bottom-hat transforms are employed. This paper proposes the feature extraction method using curvelet transform in digital mammogram to distinguish the normal and abnormal breast cancer. The curvelet handles curves by utilising only a small quantity of coefficients, and it can also perform the curve discontinuities. It is a multiscale directional transform that permits sparse representation of objects with edges. The pectoral muscles and labels have been removed by the selection of Region of Interest (ROI). The proposed work uses morphological transform for preprocessing, followed by application of curvelet transform to enhance and sharpen the images finally wrapping is done by using fast Fourier transform. The features were extracted from the curvelet coefficients by applying gray level co-occurrance matrix and features were also extracted from the regional properties of the preprocessed image. The experimental result gives 98% accuracy of the classification of cancer classes using SVM classifier. From the result, it indicates that a curvelet transformation is a capable tool for analysis of digital mammogram.