Microcalcification Detection using Multiresolution Analysis based on Wavelet Transform

Early detection is the key to improve prognosis of breast cancer, which is one of the most common forms of cancer among women. Currently, the most efficient method for breast cancer early detection is mammography. An early sign of 30-50% of breast cancer incidents is the appearance of clusters of fine, granular microcalcifications, whilst 60-80% of breast carcinomas reveal microcalcification clusters upon histological examination. An efficient method for automated classification of microcalcification clusters and thus for breast cancer control is the use of Computer Aided Diagnosis (CAD) systems. One of the most powerful computing methods these systems use is the multiresolution analysis of digitized mammographic images, based on wavelet transform. In this paper, we present a comparative study of such methods which are widely used in microcalcification detection and feature extraction. The detection of microcalcifications was achieved by decomposing the mammograms into different frequency sub-bands, and reconstructing the mammogram from the sub- bands containing only high frequencies, duo to the fact that microcalcifications correspond to high frequencies in the frequency domain of the image.