Signal/background separation by wavelet packets for detection of microcalcifications in mammograms

We developed a method of weighted wavelet packets for separation of small, low contrast signals from large, inhomogeneous background. Our method was applied to the enhancement of microcalcifications on digital mammograms, which appear as small bright spots superimposed on the background representing the structure of breast tissue, for improvement of the performance of our computer-aided diagnosis scheme for detection of clustered microcalcifications. Our method first approximates signals if interest by a set of wavelets that are extracted from a wavelet packet dictionary by means of the matching pursuit algorithm. The selected set of wavelets is then subjected to a supervised learning process for optimization of the weights assigned to individual time-frequency tiles for enhancement of the microcalcifications and suppression of the background structures. In an analysis of 82 regions of interest extracted from our mammographic database, our new method showed a sensitivity of 92 percent and a specificity of 75 percent. Our new method is shown to perform better than our previous method based on the fixed-weight, orthogonal wavelet transform.