• Korean Journal of Radiology
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• v.24 no.10
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• pp.1006-1016
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• 2023

Objective: Computed tomography (CT) is an established method for the diagnosis, staging, and treatment of multiple myeloma. Here, we investigated the potential of photon-counting detector computed tomography (PCD-CT) in terms of image quality, diagnostic confidence, and radiation dose compared with energy-integrating detector CT (EID-CT). Materials and Methods: In this prospective study, patients with known multiple myeloma underwent clinically indicated whole-body PCD-CT. The image quality of PCD-CT was assessed qualitatively by three independent radiologists for overall image quality, edge sharpness, image noise, lesion conspicuity, and diagnostic confidence using a 5-point Likert scale (5 = excellent), and quantitatively for signal homogeneity using the coefficient of variation (CV) of Hounsfield Units (HU) values and modulation transfer function (MTF) via the full width at half maximum (FWHM) in the frequency space. The results were compared with those of the current clinical standard EID-CT protocols as controls. Additionally, the radiation dose (CTDIvol) was determined. Results: We enrolled 35 patients with multiple myeloma (mean age 69.8 ± 9.1 years; 18 [51%] males). Qualitative image analysis revealed superior scores (median [interquartile range]) for PCD-CT regarding overall image quality (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), edge sharpness (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), image noise (4.0 [4.0-4.0] vs. 3.0 [3.0-4.0]), lesion conspicuity (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), and diagnostic confidence (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]) compared with EID-CT (P ≤ 0.004). In quantitative image analyses, PCD-CT compared with EID-CT revealed a substantially lower FWHM (2.89 vs. 25.68 cy/pixel) and a significantly more homogeneous signal (mean CV ± standard deviation [SD], 0.99 ± 0.65 vs. 1.66 ± 0.5; P < 0.001) at a significantly lower radiation dose (mean CTDIvol ± SD, 3.33 ± 0.82 vs. 7.19 ± 3.57 mGy; P < 0.001). Conclusion: Whole-body PCD-CT provides significantly higher subjective and objective image quality at significantly reduced radiation doses than the current clinical standard EID-CT protocols, along with readily available multi-spectral data, facilitating the potential for further advanced post-processing.

• Journal of Biomedical Engineering Research
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• v.42 no.5
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• pp.201-210
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• 2021

In Korean medicine, tongue diagnosis is one of the important diagnostic methods for diagnosing abnormalities in the body. Representative features that are used in the tongue diagnosis include color, shape, texture, cracks, and tooth marks. When diagnosing a patient through these features, the diagnosis criteria may be different for each oriental medical doctor, and even the same person may have different diagnosis results depending on time and work environment. In order to overcome this problem, recent studies to automate and standardize tongue diagnosis using machine learning are continuing and the basic process of such a machine learning-based tongue diagnosis system is tongue segmentation. In this paper, image data is augmented based on the main tongue features, and backbones of various famous deep learning architecture models are used for automatic tongue segmentation. The experimental results show that the proposed augmentation technique improves the accuracy of tongue segmentation, and that automatic tongue segmentation can be performed with a high accuracy of 99.12%.

• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.118-124
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• 2023

Many recent studies have reported that the quality of input learning data was vital to the detection of regions of interest. However, due to a lack of research on the quality of learning data on lesion detetcting using gastroscopy, we aimed to quantify the impact of quality difference in endoscopic images to lesion detection models using Image Quality Assessment (IQA) algorithms. Through IQA methods such as BRISQUE (Blind/Referenceless Image Spatial Quality Evaluation), Laplacian Score, and PSNR (Peak Signal-To-Noise) algorithm on 430 sheets of high quality data (HQD) and 430 sheets of low quality data (PQD), we showed that there were significant differences between high and low quality images in lesion detecting through BRISQUE and Laplacian scores (p<0.05). The PSNR value showed 10.62±1.76 dB on average, illustrating the lower lesion detection performance of PQD than HQD. In addition, F1-Score of HQD showed higher detection performance at 77.42±3.36% while F1-Score of PQD showed 66.82±9.07%. Through this study, we hope to contribute to future gastroscopy lesion detection assistance systems that involve IQA algorithms by emphasizing the importance of using high quality data over lower quality data.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.601-606
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• 2006

The image data amount that used in medical institution with great development of medical technology is increasing rapidly. Therefore, people need automation method that use image processing description than macrography of doctors for analysis many medical image. In this paper. we propose that acquire texture information to using GLCM about liver area of abdomen CT image, and automatically detects liver tumor using PCA from this data. Method by one feature as intensity of existent liver humor detection was most but we changed into 4 principal component accumulation images using GLCM's texture information 8 feature. Experiment result, 4 principal component accumulation image's variance percentage is 89.9%. It was seen this compare with liver tumor detecting that use only intensity about 92%. This means that can detect liver tumor even if reduce from dimension of image data to 4 dimensions that is the half in 8 dimensions.

• Journal of Multimedia Information System
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• v.5 no.1
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• pp.35-42
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• 2018

Direct volume rendering (DVR) is an important 3D visualization method for medical images as it depicts the full volumetric data. However, because DVR renders the whole volume, regions of interests (ROIs) such as a tumor that are embedded within the volume maybe occluded from view. Thus, conventional 2D cross-sectional views are still widely used, while the advantages of the DVR are often neglected. In this study, we propose a new visualization algorithm where we augment the 2D slice of interest (SOI) from an image volume with volumetric information derived from the DVR of the same volume. Our occlusion-based DVR augmentation for SOI (ODAS) uses the occlusion information derived from the voxels in front of the SOI to calculate a depth parameter that controls the amount of DVR visibility which is used to provide 3D spatial cues while not impairing the visibility of the SOI. We outline the capabilities of our ODAS and through a variety of computer tomography (CT) medical image examples, compare it to a conventional fusion of the SOI and the clipped DVR.

• Journal of Korean Society for Quality Management
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• v.41 no.2
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• pp.261-276
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• 2013

Purpose: This study intends to offer strategic implications that can be used in Korean medicine hospitals through analysis of causal relationship among factors focusing on image improvement and trust building. Methods: Differential model was introduced to test causal relationship. Questionnaire was developed, and data was collected and analyzed with Structural Equation Modeling. Results: Medical service has effects on image, trust, and CS. CS has an effect on trustworthiness, and trustworthiness has positive effect on loyalty intention and has negative effect on switching intention. Emotional attachment has moderating functions between trust and loyalty intention and between trust and switching intention. Conclusion: This study offers practical implications to relevant managers, at the same time it has limitations that omits relevant study of inducing factor for emotional attachment.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.132-137
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• 2019

The amount of data generated from medical images is increasingly exceeding the limits of professional visual analysis, and the need for automated medical image analysis is increasing. For this reason, this study evaluated the classification and accuracy according to the presence or absence of tumor using Inception V3 deep learning model, using MRI medical images showing normal and tumor findings. As a result, the accuracy of the deep learning model was 90% for the training data set and 86% for the validation data set. The loss rate was 0.56 for the training data set and 1.28 for the validation data set. In future studies, it is necessary to secure the data of publicly available medical images to improve the performance of the deep learning model and to ensure the reliability of the evaluation, and to implement modeling by improving the accuracy of labeling through labeling classification.

• Journal of Radiopharmaceuticals and Molecular Probes
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• v.3 no.2
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• pp.65-71
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• 2017

To assess the effects of filter and reconstruction of Cu-64 PET data on Siemens scanner, the various reconstruction algorithm with various filters were assessed in terms of spatial resolution, non-uniformity (NU), recovery coefficient (RC), and spillover ratio (SOR). Image reconstruction was performed using filtered backprojection (FBP), 2D ordered subset expectation maximization (OSEM), 3D reprojection algorithm (3DRP), and maximum a posteriori algorithms (MAP). For the FBP reconstruction, ramp, butterworth, hamming, hanning, or parzen filters were used. Attenuation or scatter correction were performed to assess the effect of attenuation and scatter correction. Regarding spatial resolution, highest achievable volumetric resolution was $3.08mm^3$ at the center of FOV when MAP (${\beta}=0.1$) reconstruction method was used. SOR was below 4% for FBP when ramp, Hamming, Hanning, or Shepp-logan filter were used. The lowest NU (highest uniform) after attenuation & scatter correction was 5.39% when FBP (parzen filter) was used. Regarding RC, 0.9 < RC < 1.1 was obtained when OSEM (iteration: 10) was used when attenuation and scatter correction were applied. In this study, image quality of Cu-64 on Siemens Inveon PET was investigated. This data will helpful for the quantification of Cu-64 PET data.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.632-639
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• 2021

In this paper, we proposed a reversible data hiding technique that greatly enhances the security of confidential data by encrypting confidential data and then spatially encrypting the encrypted confidential data and hiding it in the cover image. When a result image is generated by hiding the encrypted confidential data in the cover image using a spatial encryption technique, the quality of the result image is very good, and the original cover image and the result image cannot be visually distinguished. Since the encrypted confidential data is spatially encrypted and concealed, it is not possible to know where the encrypted confidential data is concealed in the result image, and the encrypted confidential data cannot be extracted from the result image. Even if the encrypted confidential data is extracted, the original confidential data is not known because the confidential data is encrypted. Therefore, if confidential data is concealed in images using the proposed technique, the security of confidential data is greatly improved. The proposed technique can be effectively used in medical and military applications.

• Journal of Biomedical Engineering Research
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• v.19 no.2
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• pp.189-198
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• 1998

3D medical image reconstruction techniques are useful to figure out complex 3D structures from the set of 2D sections. In the paper, 3D medical image reconstruction system is constructed under PC environment and programmed based on modular programming by using Visual C++ 4.2. The whole procedures are composed of data preparation, gradient estimation, classification, shading, transformation and ray-casting & compositing. Three speed optimization techniques are used for accelerating 3D medical image reconstruction technique. One is to reduce the rays when cast rays to reconstruct 3D medical image, another is to reduce the voxels to be calculated and the other is to apply early ray termination. To implement 3D medical image reconstruction system based on PC, speed optimization techniques are experimented and applied.