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.
Index Terms—Image denoising, Image segmentation, performance parameters, derivative based image denoising, clustering methods.
Sreedhar Kollem is with the JNTUH University, Telangana, India and also Dept of ECE, S R Engineering College (Autonomous), Warangal, India (e-mail: email@example.com).
K. Rama Linga Reddy was with G. Narayanamma Institute of Technology and Science, Hyderabad, Telangana, India. He is now with the Department of ETM (e-mail: firstname.lastname@example.org).
D. srinivasa Rao is with the ECE Department, JNTUH College of engineering, Hyderabad, Telangana, India (e-mail: email@example.com).
Cite: Sreedhar Kollem, Katta Rama Linga Reddy, and Duggirala Srinivasa Rao, "A Review of Image Denoising and Segmentation Methods Based on Medical Images," International Journal of Machine Learning and Computing vol. 9, no. 3, pp. 288-295, 2019.