Medical practitioners are increasingly using digital images during disease diagnosis. Several state-of-the-art medical equipment are producing images of different organs, which are used during various stages of analysis. Examples of such equipment include MRI, CT, ultrasound and X-Ray. In medical image processing, image denoising has become a very essential exercise all through the diagnosis as Ultrasound images are normally affected by speckle noise. The noise in the image has two negative outcomes, the first being the degradation of the image quality and the second and more important, obscures important information required for accurate diagnosis.Arbitration between the perpetuation of useful diagnostic information and noise suppression must be treasured in medical images. In general we rely on the intervention of a proficient to control the quality of processed images. In certain cases, for instance in Ultrasound images, the noise can suppress the information which is valuable for the general practitioner. Consequently medical images can be very inconsistent, and it is crucial to operate case to case. This paper presents a wavelet-based thresholding scheme for noise suppression in Ultrasound images and provides the knowledge about adaptive and anisotropic diffusion techniques for speckle noise removal from different types of images, like Ultrasound.