353 De-noising Analysis of Mammogram Images in the Wavelet Domain using Hard and Soft Thresholding Saima Anwar Lashari Faculty of Computer Science & Information System Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia. * hi120040@siswa.uthm.edu.my Rosziati Ibrahim Faculty of Computer Science & Information System Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia. rosziati@uthm.edu.my Norhalina Senan Faculty of Computer Science & Information System Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia. halina@uthm.edu.my Abstract—The noisy nature of digital mammograms and low contrast of suspicious areas which make medical images de-noising a challenging problem. Therefore, image de-noising is an important task in image processing, thus the use of wavelet transform provides better and improved quality of an image and reduces noise level. For medical images, many wavelets like db1, sym8, coif1, coif3 etc can be used for de- noising of a medical image. However, in this paper, haar, sym8 daubechies db3 (mallat), daubechies db4 at certain level of soft and hard threshold have been calculated. Later, peak signal to noise ratio (PSNR) values are calculated for these wavelet methods. These experiments help to select the best wavelet transform for the de-noising of particular medical images such as mammogram images. Keywords—Wavelet de-noising, hard Thresholding, soft Thresholding, Peak Signal-to-Noise Ratio I. INTRODUCTION The noise present in the images may appear as additive or multiplicative components and the main purpose of denoising is to remove these noisy components while preserving the important signal as much as possible [1]. Therefore, de-noising plays a very important role in the field of the medical image pre-processing. It is often done before the image is to be analyzed. De-noising is mainly used to remove noise that is present and retains the significant information, regardless of the frequency contents of the signal. During the process of de-noising, much attention is kept on how well the edges are preserved and how much of noise granularity been removed. Thus, the main purpose of image denoising algorithm is to eliminate the unwanted noise level while preserving the important features of an image. Unlike Fourier transform, wavelet transforms shows localization in both time and frequency and hence it has proved itself to be an efficient tool for a number of image processing including noise removal [2]. Fourier transform based methods are less useful because, they cannot work on non-stationary signals and they can capture only global features. But in real scenario, as the images are only piecewise smooth and the noise distributions are random in nature, Fourier transform cannot perform well for the stochastic noise, but wavelets can do. Hence, wavelets based noise removal has attracted much attention of the researchers for several years [3]. Thus, the objective of this paper is to see the viability of wavelet domain using hard and soft Thresholding for de-nosing mammogram images. In the wavelet domain, the noise is uniformly spread throughout the coefficients while mostly the image information is concentrated in the few largest coefficients. The most important way of distinguishing information from noise in the wavelet domain consists of Thresholding the wavelet coefficients. Mainly hard and soft Thresholding techniques are performed [4]. The organization of this paper is as follows: a brief review of wavelet Thresholding, hard and soft threshold function are presented in Section 2, the modeling process is given in Section 3, and experimental results are shown in Section 4. Finally Section 5 presents the conclusion. II. WAVELET THRESHOLDING DE-NOISING Wavelet Thresholding de-noising is based on the idea that the energy of the signal to be defined concentrates on some wavelet coefficients, while the energy of noise spreads throughout all wavelet coefficients. Similarity between the basic wavelet and the signal to be defined plays a very important role, making it possible for the signal to concentrate on fewer coefficients. The components of the impulse should be made as prominent as possible so as to improve the performance of impulse isolation. Wavelet threshold de-noising is a very efficient method, the purpose