(untitled) (Global Journal of Medical Research, 2017)
Image denoising is an important pre-processing step in medical image analysis. The basic intent of image denoising is to reconstruct the original image from its noisy observation as accurately as possible, while preserving important detail features such as edges and textures in the denoised image. In medical imaging, for the precise analysis of diseases denoising of medical images like X-RAY, CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography) and SPECT (Single Photon Emission Computed Tomography) is essential since a small lose of a particular area in case of medical images may results in immense disaster similar to death. To mitigate such threat over the last few decades, image denoising has been extensively studied in the image and signal processing community and suggested various denoising techniques. Each approach has its assumptions, advantages, and limitations. In this paper a detailed survey has been carried out on various image denoising approaches and their performances on on medical images.