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Classification of Mammograms Tumors Using Fourier Analysis


Osama R.Shahin, Gamal Attiya


Vol. 14  No. 2  pp. 110-115


Breast cancer is one of the most common cancers among women in the developing countries. It has become a major cause of death. In this work a new algorithm for classifying mammograms by using an evolutionary approach known as signatures- distances from the centroid to all points on the boundary of the region of interest (ROI) as a function of a polar angle θ. The signature of a closed boundary is a periodic function, repeating itself on an angular scale of 2π. Then encode and describe this closed boundary to arbitrary function through 1-D (radial) Fourier expansion coefficients. The method was tested over several images from the image databases taken from Breast Imaging Reporting and Data System BIRADS developed by the American College of Radiology, and from MIAS (Mammogram Image Analysis Society, UK), that provides a standardized classification for mammographic studies. The implementation of the algorithm was carried out using MATLAB codes programming and thus is capable of executing effectively on a simple personal computer with digital mammogram as accumulated data for assessment. In this paper, we describe the formatting guidelines for IJCA Journal Submission.


Microcalcifications, Mass lesions, Signature, Fourier expansion coefficients.