A Monte Carlo simulation study for feasibility of total variation (TV) noise reduction technique using digital mouse whole body (MOBY) phantom image

Feasibility of Total Variation Noise Reduction Algorithm According to Various MR-Based PET Images in a Simultaneous PET/MR System: A Phantom Study

“…The degree of noise reduction is effective, but a blurring effect is caused by the loss of high-frequency and edge signals in the images [ 25 , 26 ]. To overcome this drawback, a total variation (TV) algorithm that is able to maintain the edge signal and reduce the noise distribution by setting the region of interest for each pixel in the image has been suggested [ 27 , 28 ]. The TV noise reduction algorithm has already been proven using radiologic images from X-rays [ 29 ].…”

“…The TV noise reduction algorithm has already been proven using radiologic images from X-rays [ 29 ]. In particular, a study by Kang et al reported that CNR and COV were significantly improved in all three planes compared with the original image when the TV algorithm was applied to a 4D small-animal CT image [ 27 ]. In addition, research by Seo K. et al confirmed that the TV algorithm greatly improved the image characteristics in the noise distribution in the frequency domain in X-ray images [ 29 ].…”

Total Variation-Based Noise Reduction Image Processing Algorithm for Confocal Laser Scanning Microscopy Applied to Activity Assessment of Early Carious Lesions

“…Total variation (TV)-based approaches, which were first introduced by Rudin et al, in 1992, have been applied to medical systems to reduce image noise while preserving edge information [11]. Many studies have been conducted to evaluate the applicability of TV-based noise reduction algorithms to various medical imaging systems [12][13][14][15][16]. Wilson et al, applied an optimization model with a TV-based noise reduction algorithm to a chest computed radiography (CR) X-ray image and reported a sensitivity of approximately 79% [12].…”

“…Wilson et al, applied an optimization model with a TV-based noise reduction algorithm to a chest computed radiography (CR) X-ray image and reported a sensitivity of approximately 79% [12]. In addition, our previous research studies reported that the TV-based noise reduction algorithm outperformed conventional filters, such as the median and Wiener filters, when applied to computed tomography (CT) and chest X-ray medical imaging systems [13,15]. However, studies on the applicability of this algorithm to CLSM images are limited.…”