A Novel Gaussian in Denoising Medical Images with Different Wavelets for Internet of Things Devices
Tamara K. Al-Shayea, Constandinos X. Mavromoustakis, Jordi Mongay Batalla, George N. Mastorakis, Mithun Mukherjee, Evangelos Pallis · 2020
Over recent years the focus on the comprehensive health-care system in IoT has become increasingly important, which considers in many ways a significant concept to promote health-care. It plays a positive role in increasing the highlight of the issue of medical disadvantage that threatens the medical diagnosis. Medical image constitutes a crucial carrier of the patient's diagnosis information. It nonetheless is exposed to several kinds of noise through transmission, and storage, which leads to impeding the full diagnosis for the patient and a loss of its quality as a medical digital image. Noise is a key factor in decreasing the image quality of different sorts of medical images (X ray, CAT scan, and MRI). Many techniques have been applied for image de-noising. The Discrete wavelet transform which is regarded as the most recent and optimum technique. This paper has been presented four levels of a discrete wavelet transform for the removal of Gaussian noise from several medical images based on diverse wavelet family transforms and median filtering. The proposed method submitted admissible results with regard to removing noise from medical images. The performance evaluation of the proposed algorithm is done by measuring the values PSNR, MSD, and NC.