Image Denoising And Enhancement Using Multiwavelet With Hard Threshold In Digital Mammographic Images

Breast cancer continues to be a significant public health problem in the world. The diagnosing mammography method is the most effective technology for early detection of the breast cancer. However, in some cases, it is difficult for radiologists to detect the typical diagnostic signs, such as masses and microcalcifications on the mammograms. Dense region in digital mammographic images are usually noisy and have low contrast. And their visual screening is difficult to view for physicians. This paper describes a new multiwavelet method for noise suppression and enhancement in digital mammographic images. Initially the image is pre-processed to improve its local contrast and discriminations of subtle details. Image suppression and edge enhancement are performed based on the multiwavelet transform. At each resolution, coefficient associated with the noise is modelled and generalized by laplacian random variables. Multiwavelet can satisfy both symmetry and asymmetry which are very important characteristics in Digital image processing. The better denoising result depends on the degree of the noise, generally its energy distributed over low frequency band while both its noise and details are distributed over high frequency band and also applied hard threshold in different scale of frequency sub-bands to limit the image. This paper is proposed to indicate the suitability of different wavelets and multiwavelet on the neighbourhood in the performance of image denoising algorithms in terms of PSNR.. Finally it compares the wavelet and multiwavelet techniques to produce the best denoised mammographic image using efficient multiwavelet algorithm with hard threshold based on the performance of image denoising algorithm in terms of PSNR values.