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Feature Extraction for Classification of Microcalcifications and Mass Lesions in Mammograms


Rabi Narayan Panda, Bijay Ketan Panigrahi, Manas Ranjan Patro


Vol. 9  No. 5  pp. 255-265


Mammography is the most contemporary option for the premature detection of breast cancer in women. Nevertheless, the opinion of the radiologist has a remarkable influence on the elucidation of the mammogram. The proposed research intends to develop an image processing algorithm for the recognition of microcalcifications and mass lesions to aid the premature detection of breast cancer. The work proposed deals with a novel approach for the extraction of features like microcalcifications and mass lesions in mammograms for early detection of breast cancer. The proposed technique is based on a three-step procedure: (a) regions of interest (ROI) specification, (b) two dimensional wavelet transformation, and (c) feature extraction based on OTSU thresholding the region of interest for the identification of microcalcifications and mass lesions. ROIs are preprocessed using a wavelet-based transformation method and a thresholding technique is applied to exclude microcalcifications and mass lesions. The method suggested for the detection of microcalcifications and mass lesions from mammogram image segmentation and analysis was tested over several images taken from mini-MIAS (Mammogram Image Analysis Society, UK) database. 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.


Mammogram, Breast Cancer, Microcalcifications, Mass lesions, wavelet transformation, OTSU thresholding