A Complete Review on Image Denoising Techniques for Medical Images

Amandeep Kaur, Guanfang Dong

Abstract: Medical imaging methods, such as CT scans, MRI scans, X-rays, and ultrasound imaging, are widely used for diagnosis in the healthcare domain. However, these methods are often affected by noise, which can lead to incorrect diagnoses. Radiologists used to rely on visual features observed through various imaging techniques to diagnose diseases in patients, but now, intelligent machines and artificial intelligence offer more accurate and early diagnoses. Over the past few decades, the classical problem of image denoising in computer vision has been extensively studied. This survey paper discusses the various techniques applicable which have tried to remove the noise from medical images. A complete overview of the problem hypothesis is stated in the paper, with an in-depth discussion on types and sources of noise and the evaluation metrics deployed, followed by the discussion and implementation of various filtering and image enhancement techniques. The section is succeeded by a comprehensive literature review conducted on leading and state-of-the-art methods in broadly four domains—frequency domain, filtering, CNN-based, Generative Adversarial Networks (GAN)-based and Transformer-based approaches. The conclusion summarises the findings and proposes the importance of image denoising, focusing on Explainable AI (XAI).

External IDs:dblp:journals/npl/KaurD23