The purpose of this study is to develop measurement program model for a human being-oriented product through the between the evaluation factors of portrait and general preferences of portraits. We added new items that are essential to the image evaluation by analysing previous studies. In this study, We identified the facial focus for the first step, and the portraits were evaluated by dividing it into objective and subjective image quality evaluation items. RSC Contrast and Dynamic Range were selected as the Objective evaluation items, and the numerical values of each image could be evaluation items, and the numerical values of each image could be evaluated by statistical analysis method. Facial Exposure, Composition, Position, Ratio, Out of focus, and Emotions and Color tone of image were selected as the Subjective evaluation items. In addition, a new face recognition algorithm is applied to judge the emotions, the manufacturer can get the information that they can analyze the people's emotion. The program developed to quantitatively and qualitatively compiles the evaluation items when evaluating portraits. The program that I developed through this study can be used an analysis program that produce the data for developing the evaluation model of the product more suitable to general users of imaging systems.
Jeong, Seonghoon;Yoon, Myonggeun;Kim, Dong Wook;Chung, Weon Kuu;Chung, Mijoo;Choi, Sang Hyoun
Progress in Medical Physics
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v.26
no.4
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pp.241-249
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2015
As radiation therapy is one of three major cancer treatment methods, many cancer patients get radiation therapy. To exposure as much radiation to cancer while normal tissues near tumor get little radiation, medical physicists make a radiotherapy plan treatment and perform quality assurance before patient treatment. Despite these efforts, unintended medical accidents can occur by some errors. In order to solve the problem, patient internal dose reconstruction methods by measuring transit dose are suggested. As feasibility study for development of patient dose verification system, inverse square law, percentage depth dose and scatter factor are used to calculate dose in the water-equivalent homogeneous phantom. As a calibration results of ionization chamber and glass dosimeter to transit radiation, signals of glass dosimeter are 0.824 times at 6 MV and 0.736 times at 10 MV compared to dose measured by ionization chamber. Average scatter factor is 1.4 and Mayneord F factor was used to apply percentage depth dose data. When we verified the algorithm using the water-equivalent homogeneous phantom, maximum error was 1.65%.
Journal of the Institute of Electronics and Information Engineers
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v.54
no.3
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pp.97-107
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2017
In the field of automatic door, the infrared rays and microwave sensor are much used as the important components in charge of the motor's operation control of open and close through the incoming signal of object recognition. In case of existing system that the sensor of the infrared rays and microwave are applied to the automatic door, there are many malfunctions by the infrared rays and visible rays of the sun. Because the automatic doors are usually installed outside of building in state of exposure. The environmental change by temperature difference occurs the noise of object recognition detection signal. With this problem, the hardware fault that the detection sensor is unable to follow the object moving rapidly within detection area makes the sensing blind spot. This fault should be improved as soon as possible. Because It influences safety of passengers who use the automatic doors. This paper conducted an experiment to improve the detection area by installing extra ultrasonic sensor besides existing detection sensor. So, this paper realize the computing circuit and detection algorithm which can correctly and rapidly process the access route of objects moving fast and the location area of fixed obstacles by applying detection and advantages of ultrasonic signal to the automatic doors. With this, It is proved that the automatic door applying ultrasonic sensor is improved detection area of blind spot sensing through field test and improvement plan is proposed.
Objective: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. Materials and Methods: Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively. Results: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 ± 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. Conclusion: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.
In study suggested clinical availability to shoulder forced traction method in term of quality of image, the patient's convenience and stability, according to whether to use of shoulder forced traction bend using computed tomography(CT) that X-ray calibration and various mathematic calibration algorithm application can be applied by AEC. To achieve this, 79 patients is complaining of cervical pain oriented that shoulder forced traction bend use the before and after acquires lateral projection scout image and transverse image. transverse image of a fixed size in concern field of pixel and figure the average HU value compare that quantitative analysis. Artifact and pixel and resolution to qualitative clinical estimation image analysis. the patient feel inconvenience degree that self-diagnosis survey that estimate. As a result, lateral projection scout image if you used shoulder forced traction bend for the depicted has been an increase in the number of a cervical vertebrae. transverse image concern field shoulder forced traction bend use the before and after for pixel and the average HU-value changes was judged to be almost irrelevant. Artifact and resolution and contrast, in qualitative analysis of the results relating the observer to the unusual result. So, the patients of 82.27% complained discomfort that use of shoulder forced traction bend in self-diagnosis survey. No merit of medical image by using of bend from result was analyzed quality of image to quantitative and qualitative method judged. Nowadays, CT is supplied possible revision of quality of radiation by reduction of slice and automatic exposure controller, etc and application of preconditioning filter process due to various mathematic revision algorithm. So, image noise by beam hardening artifact should not be a problem. shoulder forced traction bend of use no longer judged clinically availability because have not influence of image quality and give discomfort, have extra dangerousness.
Blue-ice area is a glacial ice field in ice sheet, ice shelf and glaciers where snow ablation and sublimation is larger than snowfall. As the blue-ice area has large influences on the meteorite concentration mechanism and ice mass balance, it is required to quantify the concentration of blue-ice. We analyzed spectral reflectance characteristics of blue-ice, snow and cloud by using MODIS images obtained over blue-ice areas in McMurdo Dry Valleys, East Antarctica, from 2007 to 2012. We then developed Normalized Difference Blue-ice Index (NDBI) algorithm which quantifies the concentration of blue-ice. Snow and cloud have a high reflectance in visible and near-infrared (NIR) bands. Reflectance of blue-ice is high in blue band, while that lowers in the NIR band. NDBI is calculated by dividing the difference of reflectance in the blue and NIR bands by the sum of reflectances in the two bands so that NDBI = (Blue-NIR)/(Blue + NIR). NDBI calculated from the MODIS images showed that the blue-ice areas have values ranging from 0.2 to 0.5, depending on the exposure and concentration of blue-ice. It is obviously different from that of snow and cloud that has values less than 0.2 or rocks with negative values. The change of NDBI values in the blue-ice area has higher correlation with snow depth ($R^2=0.699$) than wind speed ($R^2=0.012$) or air temperature ($R^2=0.278$), all measured at a meteorological station installed in McMurdo Dry Valleys. As the snow depth increased, the NDBI value decreased, which suggests that snow depth can be estimated from NDBI values over blue-ice areas. The NDBI algorithm developed in this study will be useful for various polar research fields such as meteorite exploration, analysis of ice mass balance as well as the snow depth estimation.
The aim of this study is to establish a new QC method that can simultaneously evaluate the resolution of the x/y plane and the z-axis by producing a phantom that can reflect exposure and reconstruction parameter of MDCT system. It was used with Aquilion ONE(Cannon Medical System, Otawara, Japan), and the examination was scanned using of 120 kV, 260 mA, and the D-FOV of 300 mm2. It produced new SSP phantom modules in which two aluminum plates inclined at 45° to a vertical axis and a transverse axis to evaluate high contrast resolution of x/y plane and z axis. And it changed factors such as the algorithm, distance from gantry iso-center. All images were reconstructed in five steps from 0.6 mm to 10.0 mm slice thickness to measure resolution of x/y plane and z-axis. The image data measured FWHM and FWTM using Profile tool of Aquarius iNtusion Edition ver. 4.4.13 P6 software(Terarecon, California, USA), and analysed SPQI and signal intensity by ImageJ program(v1.53n, National Institutes of Health, USA). It decreased by 4.09~11.99%, 4.12~35.52%, and 4.70~37.64% in slice thickness of 2.5 mm, 5.0 mm, and 10.0 mm for evaluating the high contrast resolution of x/y plane according to distance from gantry iso-center. Therefore, the high contrast resolution of the x/y plane decreased when the distance from the iso-center increased or the slice thickness increased. Additionally, the slice thicknesses of 2.5 mm, 5.0 mm, and 10.0 mm with a high algorithm increased 74.83, 15.18 and 81.25%. The FWHM was almost constant on the measured SSP graph for evaluating the accuracy of slice thickness which represents the resolution of x/y plane and z-axis, but it was measured to be higher than the nominal slice thickness set by user. The FWHM and FWTM of z-axis with axial scan mode tended to increase significantly as the distance increased from gantry iso-center than the helical mode. Particularly, the thinner slice thickness that increased error range compare with the nominal slice thickness. The SPQI increased with thick slice thickness, and that was closer to 90% in the helical scan than the axial scan. In conclusion, by producing a phantom suitable for MDCT detectors and capable of quantitative resolution evaluation, it can be used as a specific method in the management of research quality and management of outdated equipment. Thus, it is expected to contribute greatly to the discrimination of lesions in the field of CT imaging.
Purpose: Recently in diagnostics area PET/CT is using a variety of areas including oncology, as well as in cardiology, neurology, etc. While increasing in the importance of PET/CT, there are various researches in the image quality related to reconstruction method. We compared and tested Iterative 2D Reconstruction Method with True X Reconstruction method by Siemens through phantom experiment, so we can see increasing of clinical usefulness of PET/CT. Materials and Methods: We measured contrast ratio and FWHM due to evaluating images on dose and experiment using Biograph 40 True Point PET/CT (Siemens, Germany). Getting a result of contrast ratio and FWHM, we used NEMA IEC PET body phantom (Data Spectrum Corp.) and capillary tube. We used the current TrueX and the previous Iterative 2D algorithm for all images which have 10 minutes long. Also, a clinical suitability of parameter for Iterative 2D and a recommended parameter by Siemens for True X are applied to the experiment. Results: We tested FWHM using capillary tube. As a result, TrueX was less than Iterative 2D. Also, the differences of FWHM get bigger in low dose. On the other hand, we tested contrasts ratio using NEMA IEC PET body phantom. As a result, TrueX was better aspect than Iterative 2D. However, there was no difference in dose. Conclusion: In this experiment, TrueX get higher results of contrast ratio and spatial resolution than Itertive 2D through experiment. Also, in the reconstruction result through TrueX, TrueX had better aspect of resolution than Iterative 2D in low dose. However, contrast ratio had no specific difference. In other words, TrueX reconstruction method in PET/CT had higher clinical value in use because TrueX can reduce exposure of patient and had a better quality of screen.
The purpose of this study on head computed tomography scan corporate reorganization adaptive iteration algorithm using the statistical noise, and quality assessment, reduction of dose was evaluated. Head CT examinations do not apply ASIR group [A group], ASIR 50 applies a group [B group] were divided into examinations. B group of each 46.9 %, 48.2 %, 43.2 %, and 47.9 % the measured in the phantom research result of measurement of CT noise average were reduced more than A group in the central part (A) and peripheral unit (B, C, D). CT number was measured with the quantitive analytical method in the display-image quality evaluation and about noise was analyze. There was A group and difference which the image noise notes statistically between B. And A group was high so that the image noise could note than B group (31.87 HUs, 31.78 HUs, 26.6 HUs, 30.42 HU P<0.05). The score of the observer 1 of A group evaluated 73.17 on 74.2 at the result 80 half tone dot of evaluating by the qualitative evaluation method of the image by the bean curd clinical image evaluation table. And the score of the observer 1 of B group evaluated 71.77 on 72.47. There was no difference (P>0.05) noted statistically. And the inappropriate image was shown to the diagnosis. As to the exposure dose, by examination by applying ASIR 50 % there was no decline in quality of the image, 47.6 % could reduce the radiation dose. In conclusion, if ASIR is applied to the clinical part, it is considered with the dose written much more that examination is possible. And when examination, it is considered that it becomes the positive factor when the examiner determines.
As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.
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