Detection of microcalcifications in digital mammograms using the dual - tree complex wavelet transform

In this paper we propose an approach to detect microcalcifications in digital mammograms using the dual-tree complex wavelet transform (DT-CWT).The approach follows four basic strategies, namely, image denoising, band suppression, morphological transformation and inverse complex wavelet transform. Recently, the DT-CWT has shown a good performance in applications that involve image processing due to more data phase information, shift invariance, and directionality than other wavelet transforms. The procedure of image denoising is carried out with a thresholding algorithm that computes recursively the optimal threshold at each level of wavelet decomposition. In order to maximise the detection a morphological conversion is then proposed and applied to the high frequencies subbands of the wavelet transformation. This procedure is applied to a set of digital mammograms from the mammography image analysis society (MIAS) database. Experimental results show that the proposed denoising algorithm and morphological transformation in combination with the DT-CWT procedure performs better than the stationary and discrete wavelet transforms and the top-hat filtering. The approach reported in this paper seems to be meaningful to aid in the results on mammogram interpretation and to get an earlier and opportune diagnostic for breast cancer.