Abstract
Mammography, the most commonly used diagnostic technique is used for early detection of breast cancer. As mammograms are low contrast and noisy images, it is essential to reduce noise while preserving fine details and edges. In order to obtain efficient diagnosis, a constructive analysis curvelet is used to provide optimal sparse representation of smooth objects and edges. Hence, in this work, a novel noise reduction method using curvelet transform is proposed for digital mammogram images. Here, digital curvelet transform is applied to the mammogram images which are embedded in random, salt and pepper, Poisson, speckle and Gaussian noises. As curvelet coefficients occur at all scales, locations and orientations, proposed thresholding algorithm applied at the expansions lead to better denoising. And it also offers exact reconstruction of images that exhibit higher perceptual quality in recovery of edges. The PSNR values of the enhanced images are higher than related earlier techniques.
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