Review on Medical Image Denoising Techniques

In recent times, with rapid growth in technology, medical imaging has become popular in healthcare. The impression of these improvements in the medical field has become evident that technology can able to diagnose disease in a much better way than before. Medical images play a vital role in the detection and prediction of disease but these images may tend to corrupt by some kind of noise or artifacts during the image acquisition process. The presence of noise makes images unclear, fine details and features of the original image are lost which leads to inaccurate detection of disease. Hence, different denoising methods are required to eliminate noise without losing image features (edges, corners, and other sharp structures). Researchers have already proposed different tools and techniques to reduce noise. Each technique has its merits and demerits. Hence preprocessing of medical images is a mandatory and essential process to get accurate results. This review article provides a comprehensive survey of different noises, denoising models, contrast enhancement methods, quality matrices. In addition, the main aim of this paper is to conduct a detailed analysis of various preprocessing techniques used on medical images which include Computed Tomography (CT), Magnetic Resonance images (MRI), Positron Emission Tomography (PET), 2D/3DULTRASOUND images.