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

Kim, Hee‐Eun; Kang, Seong-Hyeon; Kim, Kyuseok; Lee, Youngjin

doi:10.3390/app10124090

Cited by 7 publications

(5 citation statements)

References 19 publications

(23 reference statements)

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“…The image processing technology was also used in the CPM field using digital images or UAVs of construction activities [22,23]. Point cloud data sets were similarly used to evaluate the progress in construction buildings through laser scanning technology [19,24]. However, researchers used more than one sensing technology for more robustness and better results; for example, a study conducted by [25] used more than one 3D sensing technology (UWB system and laser scanner).…”

Section: Previous Studiesmentioning

confidence: 99%

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Automated Schedule and Cost Control Using 3D Sensing Technologies

2023

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Nowadays, many construction projects in KSA still struggle with cost overruns and delay in activities. Therefore, automatic monitoring approaches are needed in the construction progress monitoring domain (CPM) to address these concerns. Thus, this paper proposed a system integrating a BIM-planned model with site laser scans, as laser scanners showed massive potential in the CPM domain. The algorithms of the proposed system recognized 3D objects based on the intersection between models, alignment accuracy, and Lalonde features. The proposed system combined 3D object recognition technology with 5D information data into a 5D progress tracking system using earned value (EV) principles. The reason behind that is a lack of research regarding conducting a 5D assessment integrated BIM with 3D sensing technology in the CPM domain. The proposed system was verified using field data from a superstructure construction project where the object recognition indicators showed a 98% recall and 99% precision in recognizing 3D objects. The proposed system also used a color-coding system to address the condition of each element based on its recognition and scheduling state and address any occlusions while calculating the recognized objects. The results also revealed an automatically updated status of the project’s progress in terms of schedule(4D) and cost(5D). The automated results were also validated with a manual calculation, where a slight variation (1.35%) was observed between those calculations. This system demonstrates a degree of accurate progress tracking, automatically exceeding manual performance with less computational time.

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Section: Previous Studiesmentioning

confidence: 99%

“…Another study used a combination of RFID and laser scanning technology [26]. Other studies have used a fusion of image processing methods and laser scanners [24,[27][28][29].…”

Section: Previous Studiesmentioning

confidence: 99%

Automated Schedule and Cost Control Using 3D Sensing Technologies

2023

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“…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 ]. Recently, a study on the applicability of the TV algorithm to confocal laser scanning microscopy images was also conducted, and a quantitative analysis method of noise level for two color channels was also proposed [ 30 ]. However, there is little research in the nuclear medicine field, especially PET/MR images, that has been recently developed.…”

Section: Introductionmentioning

confidence: 99%

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

2021

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Recently, the total variation (TV) algorithm has been used for noise reduction distribution in degraded nuclear medicine images. To acquire positron emission tomography (PET) to correct the attenuation region in the PET/magnetic resonance (MR) system, the MR Dixon pulse sequence, which is based on controlled aliasing in parallel imaging, results from higher acceleration (CAIPI; MR-ACDixon-CAIPI) and generalized autocalibrating partially parallel acquisition (GRAPPA; MR-ACDixon-GRAPPA) algorithms are used. Therefore, this study aimed to evaluate the image performance of the TV noise reduction algorithm for PET/MR images using the Jaszczak phantom by injecting 18F radioisotopes with PET/MR, which is called mMR (Siemens, Germany), compared with conventional noise-reduction techniques such as Wiener and median filters. The contrast-to-noise (CNR) and coefficient of variation (COV) were used for quantitative analysis. Based on the results, PET images with the TV algorithm were improved by approximately 7.6% for CNR and decreased by approximately 20.0% for COV compared with conventional noise-reduction techniques. In particular, the image quality for the MR-ACDixon-CAIPI PET image was better than that of the MR-ACDixon-GRAPPA PET image. In conclusion, the TV noise-reduction algorithm is efficient for improving the PET image quality in PET/MR systems.

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“…Moreover, the application of noise reduction algorithms can aggravate the effect of this undesirable phenomenon [ 14 , 15 , 16 ]. Accordingly, noise reduction algorithms using the latest technologies, such as total variation, wavelet transform, deep-learning, and non-local means (NLM), have been proposed to minimize the blurring effect and selectively remove only noise [ 17 , 18 , 19 , 20 ]. Among the noise reduction algorithms mentioned above, the NLM algorithm is a known method that can smooth the pixel value inside the edge structure and maintain the high-frequency signal by calculating the intensity and distance of neighboring pixels [ 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images

Kang

Kim

2021

IJERPH

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The purpose of this study is to evaluate the various control parameters of a modeled fast non-local means (FNLM) noise reduction algorithm which can separate color channels in light microscopy (LM) images. To achieve this objective, the tendency of image characteristics with changes in parameters, such as smoothing factors and kernel and search window sizes for the FNLM algorithm, was analyzed. To quantitatively assess image characteristics, the coefficient of variation (COV), blind/referenceless image spatial quality evaluator (BRISQUE), and natural image quality evaluator (NIQE) were employed. When high smoothing factors and large search window sizes were applied, excellent COV and unsatisfactory BRISQUE and NIQE results were obtained. In addition, all three evaluation parameters improved as the kernel size increased. However, the kernel and search window sizes of the FNLM algorithm were shown to be dependent on the image processing time (time resolution). In conclusion, this work has demonstrated that the FNLM algorithm can effectively reduce noise in LM images, and parameter optimization is important to achieve the algorithm’s appropriate application.

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

Cited by 7 publications

References 19 publications

Automated Schedule and Cost Control Using 3D Sensing Technologies

Automated Schedule and Cost Control Using 3D Sensing Technologies

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

Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images

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