Optimizing wavelet transform based on supervised learning for detection of microcalcifications in digital mammograms

A novel technique for optimizing the wavelet transform to enhance and detect microcalcifications in mammograms was developed based on the supervised learning method. In the learning process, a cost function is formulated to represent the difference between a desired output and the reconstructed image obtained from weighted wavelet coefficients for a given mammogram. This cost function is then minimized by modifying the weights for wavelet coefficients via a conjugate gradient algorithm. The Least Asymmetric Daubechies' wavelets were optimized with 44 regions-of-interest as the training set using a jackknife method. The performance of the optimized wavelets achieved a sensitivity of 90% with specificity of 80%, which outperforms the authors' current scheme based on a conventional wavelet transform.