• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.240-252
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• 2023

Purpose To assess the effect of deep learning image reconstruction (DLIR) for head CT in pediatric patients. Materials and Methods We collected 126 pediatric head CT images, which were reconstructed using filtered back projection, iterative reconstruction using adaptive statistical iterative reconstruction (ASiR)-V, and all three levels of DLIR (TrueFidelity; GE Healthcare). Each image set group was divided into four subgroups according to the patients' ages. Clinical and dose-related data were reviewed. Quantitative parameters, including the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and qualitative parameters, including noise, gray matter-white matter (GM-WM) differentiation, sharpness, artifact, acceptability, and unfamiliar texture change were evaluated and compared. Results The SNR and CNR of each level in each age group increased among strength levels of DLIR. High-level DLIR showed a significantly improved SNR and CNR (p < 0.05). Sequential reduction of noise, improvement of GM-WM differentiation, and improvement of sharpness was noted among strength levels of DLIR. Those of high-level DLIR showed a similar value as that with ASiR-V. Artifact and acceptability did not show a significant difference among the adapted levels of DLIR. Conclusion Adaptation of high-level DLIR for the pediatric head CT can significantly reduce image noise. Modification is needed while processing artifacts.

• Korean Journal of Radiology
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• v.23 no.4
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• pp.389-401
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• 2022

Objective: This study aimed to determine a factor for predicting suboptimal image quality of the hepatobiliary phase (HBP) of gadoxetic acid-enhanced MRI in patients with extrahepatic bile duct (EHD) cancer before MRI examination. Materials and Methods: We retrospectively evaluated 259 patients (mean age ± standard deviation: 68.0 ± 8.3 years; 162 male and 97 female) with EHD cancer who underwent gadoxetic acid-enhanced MRI between 2011 and 2017. Patients were divided into a primary analysis set (n = 184) and a validation set (n = 75) based on the diagnosis date of January 2014. Two reviewers assigned the functional liver imaging score (FLIS) to reflect the HBP image quality. The FLIS consists of the sum of three HBP features, each scored on a 0-2 scale: liver parenchymal enhancement, biliary excretion, and signal intensity of the portal vein. Patients were classified into low-FLIS (0-3) or high-FLIS (4-6) groups. Multivariable analysis was performed to determine a predictor of low FLIS using serum biochemical and imaging parameters of cholestasis severity. The optimal cutoff value for predicting low FLIS was obtained using receiver operating characteristic analysis, and validation was performed. Results: Of the 259 patients, 140 (54.0%) and 119 (46.0%) were classified into the low-FLIS and high-FLIS groups, respectively. In the primary analysis set, total bilirubin was an independent factor associated with low FLIS (adjusted odds ratio per 1-mg/dL increase, 1.62; 95% confidence interval [CI], 1.32-1.98). The optimal cutoff value of total bilirubin for predicting low FLIS was 2.1 mg/dL with a sensitivity of 95.1% (95% CI: 88.9-98.4) and a specificity of 89.0% (95% CI: 80.2-94.9). In the validation set, the total bilirubin cutoff showed a sensitivity of 92.1% (95% CI: 78.6-98.3) and a specificity of 83.8% (95% CI: 68.0-93.8). Conclusion: Serum total bilirubin before acquisition of gadoxetic acid-enhanced MRI may help predict suboptimal HBP image quality in patients with EHD cancer.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.621-628
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• 2021

This paper proposes a method to calibrate a thermal camera with three different perspectives. In particular, the intrinsic parameters of the camera and re-projection errors were provided to quantify the accuracy of the calibration result. Three lenses of the camera capture the same image, but they are not overlapped, and the image resolution is worse than the one captured by the RGB camera. In computer vision, camera calibration is one of the most important and fundamental tasks to calculate the distance between camera (s) and a target object or the three-dimensional (3D) coordinates of a point in a 3D object. Once calibration is complete, the intrinsic and the extrinsic parameters of the camera(s) are provided. The intrinsic parameters are composed of the focal length, skewness factor, and principal points, and the extrinsic parameters are composed of the relative rotation and translation of the camera(s). This study estimated the intrinsic parameters of thermal cameras that have three lenses of different perspectives. In particular, image enhancement based on a deep learning algorithm was carried out to improve the quality of the calibration results. Experimental results are provided to substantiate the proposed method.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.481-490
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• 2002

Off-line handwritten character recognition is in difficulty of incomplete preprocessing because it has not dynamic information has various handwriting, extreme overlap of the consonant and vowel and many error image of stroke. Consequently off-line handwritten character recognition needs to study about preprocessing of various methods such as binarization and thinning. This paper considers running time of watershed algorithm and the quality of resulting image as preprocessing for off-line handwritten Korean character recognition. So it proposes application of effective watershed algorithm for segmentation of character region and background region in gray level character image and segmentation function for binarization by extracted watershed image. Besides it proposes thinning methods that effectively extracts skeleton through conditional test mask considering routing time and quality of skeleton, estimates efficiency of existing methods and this paper's methods as running time and quality. Average execution time on the previous method was 2.16 second and on this paper method was 1.72 second. We prove that this paper's method removed noise effectively with overlap stroke as compared with the previous method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.728-735
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• 2017

With the proliferation of digital devices, the devices have generated considerable additive white Gaussian noise while acquiring digital images. The most well-known denoising methods focused on eliminating the noise, so detailed components that include image information were removed proportionally while eliminating the image noise. The proposed algorithm provides a method that preserves the details and effectively removes the noise. In this proposed method, the goal is to separate meaningful detail information in image noise environment using the edge strength and edge connectivity. Consequently, even as the noise level increases, it shows denoising results better than the other benchmark methods because proposed method extracts the connected detail component information. In addition, the proposed method effectively eliminated the noise for various noise levels; compared to the benchmark algorithms, the proposed algorithm shows a highly structural similarity index(SSIM) value and peak signal-to-noise ratio(PSNR) value, respectively. As shown the result of high SSIMs, it was confirmed that the SSIMs of the denoising results includes a human visual system(HVS).

• Journal of Broadcast Engineering
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• v.22 no.6
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• pp.702-712
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• 2017

In this paper, we propose an unsharp masking algorithm using Laplacian as a weight map for the signal structure and a gamma transformation algorithm using image mean intensity as a weight map for mean intensity. The conventional weight map based on the patch has a disadvantage in that the brightness in the image is shifted to one side in the signal structure and the mean intensity region. So the detailed information is lost. In this paper, we improved the detail using unsharp masking of patch unit and proposed linearly combined the gamma transformed values using the average brightness values of the global and local images. Through the proposed algorithm, the detail information such as edges are preserved and the subjective image quality is improved by adjusting the brightness of the light. Experiment results show that the proposed algorithm show better performance than conventional algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.111-120
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• 2005

A cost effective three-dimensional color gamut mapping architecture is described. The conventional three-dimensional reduced resolution look-up table is considered and the concept of three-dimensional reduced resolution difference look-up table is introduced for cost effective and real-time color gamut mapping. The overall architecture uses one-dimensional memory decomposition of three-dimensional gamut mapping look-up table, three-dimensional interpolation and simple addition operation for generating the final gamut mapped colors. The required computational cost is greatly reduced by look-up table resolution adjustment and further reduced by the gamut mapping rule modification. The proposed architecture greatly reduces the required memory size and hardware complexity compared to the conventional method and it is suitable for real-time applications. The proposed hardware is suitable for FPGA and ASIC implementation and could be applied to the real-time display quality enhancement purposes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1853-1859
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• 2008

In this paper, we propose a novel method to improve the visual quality of reconstructed images for 3D pattern recognition based on the computational integral imaging reconstruction (CIIR). The proposed CIIR method provides improved 3D reconstructed images by superimposing magnified elemental images by a combination of a round mapping model and image interpolation algorithms. To objectively evaluate the proposed method, we introduce an experimental framework for a computational pickup process and a CIIR process using a Gaussian function and evaluate the proposed method. We also carry out experiments on 3D objects and present their results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.55-62
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• 2019

We examined the Audit Quality on the Socially Responsible Investment(SRI) Corporate. We used 1,497 sample data from 2014 to 2016. In short, the result of this paper's is as followed. Socially Responsible Investment(SRI) has a positive relevance with Audit Quality. Socially Responsible Investment(SRI) has a positive relevance with Audit Fee, Audit Time and Audit Size specifically. Therefore we can support that a firm has a high level of Socially Responsible Investment(SRI) will have the better the Audit Quality according to this study. This study contributes as follow. We can verify that the more Socially Responsible Investment(SRI) the better Quality of Accounting Information. We expect that this study can be helped positive image enhancement of Socially Responsible Investment(SRI) Corporate. So we hope that our paper can contribute sound capital market's development.

• Journal of the Korean Society of Radiology
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• v.11 no.6
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• pp.443-451
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• 2017

The purpose of this study was to comparison of measure signal to noise ratio (SNR) according to change of kernel size from region of interest (ROI) of heart shadow in chest image. We examined images of chest image of 100 patients in a University-affiliated hospital, Seoul, Korea. Chest images of each patient were calculated by using ImageJ. We have analysis socio-demographical variables, SNR according to images, 95% confidence according to SNR of difference in a mean of SNR. Differences of SNR among change of equalization were tested by SPSS Statistics21 ANOVA test for there was statistical significance 95%(p<0.05). In SNR results, with the quality of distributions in the order of kernel size 9*9 image, kernel size 7*7 image and original chest image, kernel size 3*3 image (p<0.001). In conclusion, this study would be that quantitative evaluation of heart shadow on chest image can be used as an adjunct to the kernel size chest image.