MAMMOGRAPHIC IMAGE ANALYSIS FOR BREAST CANCER DETECTION USING COMPLEX WAVELET TRANSFORMS AND MORPHOLOGICAL OPERATORS

This paper presents an approach for early diagnostic of Breast Cancer using the dual-tree complex wavelet transform (DT-CWT), which detect micro-calcifications in digital mammograms. The approach follows four basic strategies, namely, image denoising, band suppression, morphological transformation and inverse complex wavelet transform. 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 maximize the detection a morphological conversion is 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 previous reported approaches.