A review of image denoising and segmentation methods based on medical images

Abstract

Image denoising and segmentation are required to use in digital image processing. For researchers' point of view, still, these two methods are challenging task in medical images. At present, image denoising and segmentation take part in real-world applications such as computer graphic, computer vision, satellite, and medical fields. These two methods are analyzed by using different images but mainly concentration on medical images such as computed tomography, single photon emission computed tomography, magnetic resonance imaging, positron emission tomography. Medical images can break into noise, major research has created solutions to this complication, various techniques are being proposed. Image segmentation is a widespread and active area not only for medical imaging but also for computer vision and satellite imaging. The foremost plan of image segmentation remains to segment images into different components, which are used to give more information about the medical image. Here is an overview of the different methods after a brief introduction. These methods are classified as the basis for the techniques used.