• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.203-209
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• 2022

One of the typical methods for lowering radiation dose while maintaining image quality of computed tomography (CT) is the use of model-based iterative reconstruction (MBIR). This study is to evaluate the image quality by adjusting the strength of the advanced modeled iterative reconstruction (ADMIRE), which is well known as a representative model of MBIR. The study was conducted using phantom, and CT images were obtained while adjusting the strength of ADMIRE in units of 1 to 5. Quantitative evaluation includes noise levels using coefficient of variation (COV) and contrast to noise ratio (CNR), as well as natural image quality evaluation (NIQE) and blind/referenceless image spatial quality evaluator (BRISQUE). As a result, in both noise level and blind quality evaluation results, the higher the strength of ADMIRE, the better the results were derived. In particular, it was confirmed that COV and CNR were improved 1.89 and 1.75 times at ADMIRE 5 compared to ADMIRE 1, respectively, and NIQE and BRISQUE were proved to be improved 1.35 and 1.22 times at ADMIRE 5 compared to ADMIRE 1, respectively. In conclusion, this study was proved that the reconstruction strength of ADMIRE had a great influence on the noise level and overall image quality evaluation of CT images.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2594-2600
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• 2020

A gamma camera system using radionuclide has a functional imaging technique and is frequently used in the field of nuclear medicine. In the gamma camera, it is extremely important to improve the image quality to ensure accurate detection of diseases. In this study, we designed a blind-deconvolution framework after a noise-reduction algorithm based on a non-local mean, which has been shown to outperform conventional methodologies with regard to the gamma camera system. For this purpose, we performed a simulation using the Monte Carlo method and conducted an experiment. The image performance was evaluated by visual assessment and according to the intensity profile, and a quantitative evaluation using a normalized noise-power spectrum was performed on the acquired image and the blind-deconvolution image after noise reduction. The result indicates an improvement in image performance for gamma camera images when our proposed algorithm is used.

• Journal of the Korean Society of Radiology
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• v.15 no.3
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• pp.301-306
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• 2021

The use of tin filters as a way to reduce the medical radiation in computed tomography (CT). However, due to the changed X-ray spectrum with the use of tin filters, disease diagnosis could be affected because it appears as images of different impressions from previous images. Therefore, this study evaluates the changes in images when using tin filter and high pitch in chest low-dose CT. In this study, images were acquired in groups of three for comparison. Group 1 did not apply to tin filter, and used the existing pitch 0.8. Group 2 used a tin filter, pitch 0.8, Group 3 used a tin filter, and pitch 2.5. To compare the image quality, the natural image quality evaluator (NIQE) and the blind/referenceless image quality evaluator (BRISQUE) were used among the blind quality evaluation factors depended on a no-reference basis. As a result, the NIQE values were low in the order of Group 1, Group 3, and Group 2. BRISQUE values were low in the order of Group 3, Group 2 and Group 1. This study confirms the superiority of images of tin filter and high pitch techniques in chest low-dose CT, which is considered to be a fundamental study for acquiring accurate images of patients with difficult breathing control.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.623-628
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• 2019

The aim of this study was to design fast non local means (FNLM) noise reduction algorithm and to confirm its application feasibility in light microscopic image. For that aim, we acquired mouse first molar image and compared between previous widely used noise reduction algorithm and our proposed FNLM algorithm in acquired light microscopic image. Contrast to noise ratio, coefficient of variation, and no reference-based evaluation parameter such as natural image quality evaluator (NIQE) and blind/referenceless image spatial quality evaluator (BRISQUE) were used in this study. According to the result, our proposed FNLM noise reduction algorithm can achieve excellent result in all evaluation parameters. In particular, it was confirmed that the NIQE and BRISQUE evaluation parameters for analyzing the overall morphologcal image of the tooth were 1.14 and 1.12 times better than the original image, respectively. In conclusion, we demonstrated the usefulness and feasibility of FNLM noise reduction algorithm in light microscopic image of small animal tooth.

• Journal of radiological science and technology
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• v.47 no.3
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• pp.175-182
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• 2024

Advanced modeled iterative reconstruction (ADMIRE) represents a repetitive reconstruction method that can adjust strength and kernel, each of which are known to affect computed tomography (CT) image quality. The aim of this study was to quantitatively analyze the noise and spatial resolution of CT images according to ADMIRE control factors. Patient images were obtained by applying ADMIRE strength 2 and 3, and kernel B40 and B59. For quantitative evaluations, the noise level, spatial resolution, and overall image quality were measured using coefficient of variation (COV), edge rise distance (ERD), and natural image quality evaluation (NIQE). The superior values for the average COV, ERD, and NIQE results were obtained for the ADMIRE reconstruction conditions of ADMIRE 2 + B40, ADMIRE 3 + B59, and ADMIRE3 + B59. NIQE, which represents the overall image quality based on no-reference, was about 6.04 when using ADMIRE 3 + B59, showing the best result among the reconstructed image acquisition conditions. The results of this study indicate that the ADMIRE strength and kernel chosen for use in ADMIRE reconstruction have a significant impact on CT image quality. This highlights the importance of adjusting to the control factors in consideration of the clinical environment.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4591-4596
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• 2023

Single-photon emission computed tomography SPECT image reconstruction methods have a significant influence on image quality, with filtered back projection (FBP) and ordered subset expectation maximization (OSEM) being the most commonly used methods. In this study, we proposed newly-designed adaptive non-blind deconvolution with a structural similarity (SSIM) index that can take advantage of the FBP and OSEM image reconstruction methods. After acquiring brain SPECT images, the proposed image was obtained using an algorithm that applied the SSIM metric, defined by predicting the distribution and amount of blurring. As a result of the contrast to noise ratio (CNR) and coefficient of variation evaluation (COV), the resulting image of the proposed algorithm showed a similar trend in spatial resolution to that of FBP, while obtaining values similar to those of OSEM. In addition, we confirmed that the CNR and COV values of the proposed algorithm improved by approximately 1.69 and 1.59 times, respectively, compared with those of the algorithm involving an inappropriate deblurring process. To summarize, we proposed a new type of algorithm that combines the advantages of SPECT image reconstruction techniques and is expected to be applicable in various fields.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4043-4064
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• 2021

Night-time image quality evaluation is an urgent requirement in visual inspection. The lighting environment of night-time results in low brightness, low contrast, loss of detailed information, and colour dissonance of image, which remains a daunting task of delicately evaluating the image quality at night. A new blind quality assessment metric is presented for realistic night-time scenario through a comprehensive consideration of contrast, texture, and colour in this article. To be specific, image blocks' color-gray-difference (CGD) histogram that represents contrast features is computed at first. Next, texture features that are measured by the mean subtracted contrast normalized (MSCN)-weighted local binary pattern (LBP) histogram are calculated. Then statistical features in Lαβ colour space are detected. Finally, the quality prediction model is conducted by the support vector regression (SVR) based on extracted contrast, texture, and colour features. Experiments conducted on NNID, CCRIQ, LIVE-CH, and CID2013 databases indicate that the proposed metric is superior to the compared BIQA metrics.

• Journal of Veterinary Clinics
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• v.39 no.3
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• pp.114-120
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• 2022

The purpose of this study is to examine the status of quality control using multipurpose phantom of ultrasound equipment used in hospital of veterinary college in South Korea by using ATS-539 multipurpose phantom so as to examine quantitative and objective new image evaluation method. Specialists discussed and analyzed multipurpose phantom images acquired by using convex transducer of 10 ultrasound imaging devices, currently used in 9 veterinary colleges, at 4.0-6.0 MHz. Total 8 items that can be measured with ATS-539 multipurpose phantom including dead zone, vertical and horizontal measurement, axial/lateral resolution, sensitivity, focal zone, functional resolution and gray scale/dynamic range were evaluated. For qualitative evaluation, valid decisions were made based on dead zone, axial/lateral resolution, and gray scale/dynamic range which are resolution index, and coefficient of variation (COV) and blind referenceless image spatial quality evaluator (BRISQUE) were found to increase objectivity. As a result of experiment, all the targeted ultrasonic devices were found appropriate from qualitative evaluation items of dead zone, axial/lateral resolution, and gray scale/dynamic range. In other evaluation items, they were found to be appropriate from focal zone and vertical measurement of quantitative evaluation while inappropriate from horizontal measurement, sensitivity, and functional resolution. COV value was 0.12 ± 0.04, and BRISQUE value was 47.77 ± 2.77, both analysis results show that the noise level of all ultrasonic devices was located within tolerance range. Upon image examination using ATS-539 multipurpose phantom, they were 100% appropriate with inspection standards of dead zone, axial/lateral resolution, and gray scale/dynamic range, and besides, focal zone and functional resolution can be used as evaluation items. In the field of veterinary medicine, 8 standard items using ATS-539 multipurpose phantom and image evaluation items using COV and BRISQUE can be used as standards for quality control of ultrasonography machine.

• Journal of the Korean Society of Radiology
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• v.15 no.2
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• pp.153-158
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• 2021

X-ray image analysis is a very important field to improve the early diagnosis rate and prediction accuracy of periodontal disease. Research on the development and application of artificial intelligence-based algorithms to improve the quality of such dental X-ray images is being widely conducted worldwide. Thus, the aim of this study was to design a super-resolution algorithm for predicting periodontal disease and to evaluate its applicability in dental X-ray images. The super-resolution algorithm was constructed based on the convolution layer and ReLU, and an image obtained by up-sampling a low-resolution image by 2 times was used as an input data. Also, 1,500 dental X-ray data used for deep learning training were used. Quantitative evaluation of images used root mean square error and structural similarity, which are factors that can measure similarity through comparison of two images. In addition, the recently developed no-reference based natural image quality evaluator and blind/referenceless image spatial quality evaluator were additionally analyzed. According to the results, we confirmed that the average similarity and no-reference-based evaluation values were improved by 1.86 and 2.14 times, respectively, compared to the existing bicubic-based upsampling method when the proposed method was used. In conclusion, the super-resolution algorithm for predicting periodontal disease proved useful in dental X-ray images, and it is expected to be highly applicable in various fields in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5095-5111
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• 2019

A multi-rotor Unmanned Aerial Vehicle (UAV) system is developed to solve the manhole cover detection problem for the infrastructure maintenance in the suburbs of big city. The visible light sensor is employed to collect the ground image data and a series of image processing and machine learning methods are used to detect the manhole cover. First, the image enhancement technique is employed to improve the imaging effect of visible light camera. An imaging environment perception method is used to increase the computation robustness: the blind Image Quality Evaluation Metrics (IQEMs) are used to percept the imaging environment and select the images which have a high imaging definition for the following computation. Because of its excellent processing effect the adaptive Multiple Scale Retinex (MSR) is used to enhance the imaging quality. Second, the Single Shot multi-box Detector (SSD) method is utilized to identify the manhole cover for its stable processing effect. Third, the spatial coordinate of manhole cover is also estimated from the ground image. The practical applications have verified the outdoor environment adaptability of proposed algorithm and the target detection correctness of proposed system. The detection accuracy can reach 99% and the positioning accuracy is about 0.7 meters.