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A new methodology for the detection of microcalcifications (MCs) in mammograms is presented. Since MCs correspond to the high frequency components of the mammograms, a further multiresolution analysis is applied to these components. In particular, we seek to capture better high frequency features of a mammogram by performing a second analysis only to its high frequency components. For instance, in the first step, discrete wavelet transform (DWT) is applied to the mammograms and HH image is extracted. In the second step, the DWT is applied to the previous HH image. Then six statistical features are computed. Finally, principal component analysis is employed to reduce the number of features. The k-Nearest Neighborhood (k-NN) algorithm is employed for the classification task using cross-validation technique. A similar approach is adopted with use of the discrete Fourier transform (FT). The experimental results show strong evidence of the proposed methodology for MCs detection in digital mammograms.</p

A Wavelet-Wavelet Based Processing Approach for Microcalcifications Detection in Mammograms