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. Index Terms-Image denoising, Image segmentation, performance parameters, derivative based image denoising, clustering methods. I. INTRODUCTION Digital image processing requires a computer to work with images, so it is fit aimed at image investigation, pattern recognition, together with human reflection. By means of computer knowledge, digital image processing has several applications over different industries such as face recognition, medical imaging, computer vision, remote sensing, and medical sciences. Medical images such as computed tomography, single photon emission computed tomography, magnetic resonance imaging, positron emission tomography contains rich information, both anatomy, and functionality that can be used to diagnose, plan, and investigate operations. These images can be either a two-dimensional image or a three-dimensional image, which is solved in terms of both time and non-time. A large amount of information on these pictures can be evaluated with simple visualization, but for quantitative measurements, the object must be divided into segments. Possible quantitative measurements such as organs size, heart infarct size, muscle weakness, tumor size, as well as tumor Manuscript

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At present, digital image processing plays a vital role in medical imaging areas and specifically in magnetic resonance imaging (MRI) of brain images such as axial and coronal sections. This article mainly focused on the MRI brain images. The existing methods such as total variation (MC), parallel MRI, modified pyramidal dual‐tree direction filter, adaptive dictionary selection algorithm, classifier methods, and fuzzy clustering techniques are poor in image eminence and precision. Thus, this article presents a novel approach consisting of denoising followed by segmentation. The objective of these proposed methods was visual eminence improvement of medical images to examine tumor extent using an adaptive partial differential equation (APDE)‐based analysis with soft threshold function in denoising. The fourth order, nonlinear APDE was used to denoise the image depending on gradient and Laplacian operators associated with the new adaptive Haar‐type wavelet transform. A second approach was the new convergent K‐means clustering for segmentation. The convergent K‐means procedure diminishes the summation of the squared deviations of structures in a cluster from the center. The significance of these proposed methods was to compute their performances in terms of mean squared error, peak signal‐to‐noise ratio, structure similarity, segmentation accuracy, false hit, missed‐term, and elapsed time. The results were analyzed with the MATLAB software.

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