• KIPS Transactions on Computer and Communication Systems
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• v.10 no.11
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• pp.305-310
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• 2021

Artificial intelligence technology in the medical field initially focused on analysis and algorithm development, but it is gradually changing to web application development for service as a product. This paper describes a Urinary Stone segmentation model in abdominal CT images and an artificial intelligence web application based on it. To implement this, a model was developed using U-Net, a fully-convolutional network-based model of the end-to-end method proposed for the purpose of image segmentation in the medical imaging field. And for web service development, it was developed based on AWS cloud using a Python-based micro web framework called Flask. Finally, the result predicted by the urolithiasis segmentation model by model serving is shown as the result of performing the AI web application service. We expect that our proposed AI web application service will be utilized for screening test.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.3
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• pp.597-605
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• 1999

In this paper, we propose a cardioangiography sequence image coding scheme which use a subtraction between initial image and current frame inserted contrast dye. Stable regions are obtained by the multithreshold and meaningful region is extracted by the images with stable region. The image with meaningful region is classified into contour and texture information. Contour information is coded by contour coding. And texture information is approximated by two-dimensional polynomial function and each coefficients is coded. Experimental results confirm that the sequence of cardioangiography are well reconstructed at the low bit rate (0.02∼0.04 bpp) and high compression ratio.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.5
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• pp.55-61
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• 2007

In this paper, we propose a three-dimensional(3D) active shape models for medical image segmentation. In order to build a 3D shape model, we need to generate a point distribution model(PDM) and select corresponding landmarks in all the training shapes. The manual determination method, two-dimensional(2D) method, and limited 3D method of landmark correspondences are time-consuming, tedious, and error-prone. In this paper, we generate a 3D statistical shape model using the 3D model generation method of a distance transform and a tetrahedron method for landmarking. After generating the 3D model, we extend the shape model training and gray-level model training of 2D active shape models(ASMs) and we use the integrated modeling process with scale and gray-level models for the appearance profile to represent the local structure. Experimental results are comparable to those of region-based, contour-based methods, and 2D ASMs.

• Progress in Medical Physics
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• v.31 no.3
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• pp.111-123
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• 2020

Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

• Journal of Biomedical Engineering Research
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• v.19 no.4
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• pp.361-367
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• 1998

We student tow image characteristics, the smoothness and the similarity, which give rise to local and global redundancy in image representation. The smoothness means that the gray level values within a given block vary gradually rather than abruptly. The similarity means that any patterns in an image repeat itself anywhere in the rest of the image. In this sense, we proposed a lossless medical image compression scheme which exploits both types of redundancy. The proposed method segments the image into variable size blocks and encodes them depending on characteristics of the blocks. The proposed compression schemes works better 10~40[%] than other compression scheme such as the Huffman, the arithmetic, the Lempel-Ziv, HINT(Hierachical Interpolation) and the lossless scheme of JPEG with one predictor.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.430-437
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• 2010

It is essential for living donor liver transplantation that surgeon must understand the hepatic vessel structure to improve the success rate of operation. In this paper, we extract the liver boundary without other surrounding structures such as heart, stomach, and spleen using the contrast enhanced MDCT liver image sequence. After that, we extract the major hepatic veins (left, middle, right hepatic vein) with morphological filter after review the basic structure of hepatic vessel which reside in segmented liver image region. The purpose of this study is provide the overall status of transplantation operation with size estimation of resection part which is dissected along with the middle hepatic vein. The method of liver extraction is as follows: firstly, we get rid of background and muscle layer with gray level distribution ratio from sampling process. secondly, the coincident images match with unit mesh image are unified with resulted image using the corse coordinate of liver and body. thirdly, we extract the final liver image after expanding and region filling. Using the segmented liver images, we extract the hepatic vessels with morphological filter and reversed the major hepatic vessels only with a results of ascending order of vessel size. The 3D reconstructed views of hepatic vessel are generated after applying the interpolation to provide the smooth view. These 3D view are used to estimate the dissection line after identify the middle hepatic vein. Finally, the volume of resection region is calculated and we can identify the possibility of successful transplantation operation.

• Journal of Biomedical Engineering Research
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• v.22 no.5
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• pp.377-384
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• 2001

Generally. there have been limitations to investigate structural brain abnormalities with MR images for psychiatric patients. such as schizophrenia. depression and autism, since the brain abnormalities of psychiatric Patients are too small to be detected easily. It has been suggested to exploit the result of size-comparison or analysis of specified Part in various brain tissues. Results of brain analysis highly depend on accuracy of the brain segmentation because it is hard to segment image that the boundary between tissues in the brain MRI is inherently value. In this Paper. we improve the quality of brain segmentation so that we increase the credit of brain analysis. In addition, we Provide the improved images for studying brain abnormalities through left-right insular volume measure using handy software tool .

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.223-224
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• 2022

본 논문에서는 축형 척추관절염으로 발전할 수 있는 천장관절염 환자들을 진단하기 위해 장골의 관심영역을 자동 생성할 수 있는 세그멘테이션 방법을 제안한다. 다양한 MRI 기기로부터 얻은 천장관절염 환자의 영상에서 장골의 GT(Ground Truth)를 생성하였으며, 대장 용종 검출을 위한 세그멘테이션 모델인 PraNet과 지역 특징 간의 표현 능력을 활용할 수 있는 Position Attention Module을 사용하여 유의미한 성능 향상을 보여주었다.

• Journal of Biomedical Engineering Research
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• v.23 no.2
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• pp.101-107
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• 2002

This paper presents a segmentation algorithm to extract endocardial contour and epicardial contour of left ventricle in MR Cardiac images. The algorithm is based on a generalized gradient vector flow(GGVF) snake and a prediction of initial contour(PIC). Especially. the proposed algorithm uses physical characteristics of endocardial and epicardial contours, cross profile correlation matching(CPCM), and a mixed interpolation model. In the experiment, the proposed method is applied to short axis MR cardiac image set, which are obtained by Siemens, Medinus, and GE MRI Systems. The experimental results show that the proposed algorithm can extract acceptable epicardial and endocardial walls. We calculate quantitative parameters from the segmented results, which are displayed graphically. The segmented left vents role is visualized volumetrically by surface rendering. The proposed algorithm is implemented on Windows environment using Visual C ++.

• Journal of International Society for Simulation Surgery
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• v.1 no.2
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• pp.75-79
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• 2014

Purpose The region growing has a critical problem that it often extract vessels with unexpected objects such as a bone which has a similar intensity characteristics to the vessel. We propose the new method to extract arterial vascular anatomy around the stomach from the CTA volume without the post-processing. Materials and Methods Our method, which is also based on the region growing, requires the two seed points from the use. I automatically extracts perigastric arteries using the adaptive region growing method and it does not need any post-processing. Results The three region growing based methods are used to extract perigastric arteries - the conventional region growings with restrict and loose thresholds each and the proposed method. The 3D visualization from the result of our method shows our method extracted the all required arteries for gastric surgery. Conclusion By extracting perigastric arteries using the proposed method, over-segmentation problem that unexpected anatomical objects such as a rib or backbone are also segmented does not occurs anymore. The proposed method does not need to sensitively determine the thresholds of the similarity function. By visualizing the result, the preoperative simulation of arterial vascular anatomy around the stomach can be possible.