A review on medical image denoising algorithms. (August 2020)

• Record Type: Journal Article
• Authors: Mohd Sagheer, Sameera V.; George, Sudhish N.
• Is Part Of: Biomedical signal processing and control. Volume 61(2020)
• Publication Date: 2020-08
• Subjects: Ultrasound (US) images -- Magnetic resonance (MR) images -- Computer tomography (CT) images -- Positron emission tomography (PET) images -- Denoising; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102036
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Over the past two decades, medical imaging and diagnostic techniques have gained immense attraction due to the rapid development in computing, internet, data storage and wireless technology. The reflection of these advancements has become evident in the field of medicine and medical sciences which enables the diagnosis and treatment of various diseases in a more fruitful manner. Furthermore, medical imaging is frequently justified in the follow up of a disease which is already diagnosed and treated. Medical images like any other form of imaging techniques are susceptible to noise and artifacts. Noise can be random or white noise with an even frequency distribution or frequency dependent noise introduced by a device's mechanism or signal processing algorithms. The presence of noise makes the images unclear and may perplex the identification and analysis of diseases which may result heavy losses including deaths. Hence, denoising of medical images is a mandatory and essential pre-processing technique for further medical image processing stages. The aim of this paper is to conduct a detailed analysis of the different denoising techniques used for medical imaging modalities which include the 2 D /3 D Ultrasound (US), Magnetic Resonance (MR), Computed Tomography (CT) and Positron Emission Tomography (PET) images.