Detection of microcalcification clusters in digital mammograms using Multiresolution based foveal algorithm

Mammography is the most used diagnostic technique for breast cancer. Microcalcification clusters are the early sign of breast cancer and their early detection is a key to increase the survival rate of women. The appearance of microcalcification clusters in mammogram as small localized granular points, which is difficult to identify by radiologists because of its tiny size. An efficient method to improve diagnostic accuracy in digitized mammograms is the use of Computer Aided Diagnosis (CAD) system. This paper presents Multiresolution based foveal algorithm for microcalcification detection in mammograms. The detection of microcalcifications is achieved by decomposing the mammogram by wavelet transform without sampling operator into different sub-bands, suppressing the coarsest approximation subband, and finally reconstructing the mammogram from the subbands containing only significant detail information. The significant details are obtained by foveal concepts. Experimental results show that the proposed method is better in detecting the microcalcification clusters than other wavelet decomposition methods.