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