Abstract
In medical imaging systems, denoising is one of the important image processing tasks. Automatic noise removal will improve the quality of diagnosis and requires careful treatment of obtained imagery. Com-puted tomography (CT) and X-Ray imaging systems use the X radiation to capture images and they are usually corrupted by noise following a Poisson distribution. Due to the importance of Poisson noise re-moval in medical imaging, there are many state-of-the-art methods that have been studied in the image processing literature. These include methods that are based on total variation (TV) regularization, wave-lets, principal component analysis, machine learning etc. In this work, we will provide a review of the following important Poisson removal methods: the method based on the modified TV model, the adaptive TV method, the adaptive non-local total variation method, the method based on the higher-order natural image prior model, the Poisson reducing bilateral filter, the PURE-LET method, and the variance stabi-lizing transform-based methods. Our task focuses on methodology overview, accuracy, execution time and their advantage/disadvantage assessments. The goal of this paper is to provide an apt choice of denoising method that suits to CT and X-ray images. The integration of several high-quality denoising methods in image processing software for medical imaging systems will be always excellent option and help further image analysis for computer-aided diagnosis.
Highlights
Image denoising and noise removal with structure preservation is one of important tasks that are integrated in medical diagnostic imaging system, such as X-Ray, computed tomography (CT)
Because the Poisson noise is a type of signal dependent noises, applying the usual denoising methods like for additive noises is ineffective, we need to design specific methods based on its characteristics
The MROF, adaptive total variation method (ATV), adaptive nonlocal total variation method (ANLTV) and higher-order natural image prior model (HNIPM) methods based on regularization, their accuracy is good enough to perform in medical imaging systems
Summary
Image denoising and noise removal with structure preservation is one of important tasks that are integrated in medical diagnostic imaging system, such as X-Ray, computed tomography (CT). The noise density in these systems follows by the Poisson distribution and well known as the Poisson noise, shot noise, photon noise, Schott noise or quantum noise. Poisson noise does not depend on temperature and frequency, it depends on photon counters. Digitization is an important technique to improve image quality in medical imaging systems and the Poisson noise characteristics needs to be considered to remove it effectively [1]. Because the Poisson noise is a type of signal dependent noises, applying the usual denoising methods like for additive noises is ineffective, we need to design specific methods based on its characteristics
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
