• International Journal of Advanced Culture Technology
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• 제11권1호
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• pp.283-289
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• 2023

AI is bringing about drastic changes not only in the aspect of technologies but also in society and culture. Medical AI based on deep learning have developed rapidly. Especially, the field of medical image analysis has been proven that AI can identify the characteristics of medical images more accurately and quickly than clinicians. Evaluating the latest results of the AI-based medical image processing is important for the implication for the development direction of medical AI. In this paper, we analyze and evaluate the latest trends in AI-based medical image analysis, which is showing great achievements in the field of medical AI in the healthcare industry. We analyze deep learning models for medical image analysis and AI-based medical image segmentation for quantitative analysis. Also, we evaluate the future development direction in terms of marketability as well as the size and characteristics of the medical AI market and the restrictions to market growth. For evaluating the latest trend in the deep learning-based medical image processing, we analyze the latest research results on the deep learning-based medical image processing and data of medical AI market. The analyzed trends provide the overall views and implication for the developing deep learning in the medical fields.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권1호
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• pp.64-75
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• 2003

Medical image segmentation is the process by which an original image is partitioned into some homogeneous regions like bones, soft tissues, etc. This study demonstrates an automatic medical image segmentation technique based on independent component analysis. Independent component analysis is a generalization of principal component analysis which encodes the higher-order dependencies in the input in addition to the correlations. It extracts statistically independent components from input data. Use of automatic medical image segmentation technique using independent component analysis under the assumption that medical image consists of some statistically independent parts leads to a method that allows for more accurate segmentation of bones from CT data. The result of automatic segmentation using independent component analysis with square test data was evaluated using probability of error(PE) and ultimate measurement accuracy(UMA) value. It was also compared to a general segmentation method using threshold based on sensitivity(True Positive Rate), specificity(False Positive Rate) and mislabelling rate. The evaluation result was done statistical Paired-t test. Most of the results show that the automatic segmentation using independent component analysis has better result than general segmentation using threshold.

• 한국정보처리학회논문지
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• 제7권3호
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• pp.977-991
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• 2000

In this paper, a medical image processing system was designed and implemented for morphometric and functional analysis of a human brain. The system is composed of image registration, ROI(region of interest) analysis, functional analysis, image visualization, 3D medical image database management system(DBMS), and database. The software processes an anatomical and functional image as input data, and provides visual and quantitative results. Input data and intermediate or final output data are stored to the database as several data types by the DBMS for other further image processing. In the experiment, the ROI analysis, for a normal, a tumor, a Parkinson's decease, and a depression case, showed that the system is useful for morphometric and functional analysis of a human brain.

• 대한의용생체공학회:의공학회지
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• 제15권1호
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• pp.63-70
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• 1994

A medical image workstation was developed using multimedia technique. The system based on PC-486DX was designed to acquire medical images produced by medical imaging instruments and related audio information, that is, doctors'reporting results. Input int'ormation was processed and analyzed, then the results were presented in the form of graph and animation. All the informations of the system were hierarchically related with the image as the apex. Processing and Analysis algorithms were implemented so that the diagnostic accuracy could be improved. The diagnosed id'ormation can be transferred for patient diagnosis through LAN (local area network).

• 한국컴퓨터정보학회논문지
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• 제14권12호
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• pp.191-199
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• 2009

의료영상에 대한 영상정보와 진단정보를 공유하는 환경으로 사용되는 의료영상 시스템은 효과적인 진단 보조 도구로 활용된다. 대규모 의료기관과 협력기관들은 통합 의료정보 시스템이 구축되어 영상정보와 진단정보를 공유할 수 있다. 그러나 통합 의료정보 시스템은 단순히 정보의 저장과 전송만을 제공한다. 이러한 문제점을 해결하고 진단 활동의 효율성을 높이기 위해서는 의료영상 분석 시스템이 필요하다. 본 논문에서 제안한 관계기법은 속성 생성을 위해 의료영상을 분석하고, 본 기법 하에 의료영상은 여러 개의 객체로 분할되며, 의료영상 속성들은 분할된 영상에서 추출된다. 추출된 속성들은 의료영상 분석을 위해 관계기법에 적용된다. 몇 가지 실험 결과를 통해 제안 기법의 효과를 확인하였다.

• 한국컴퓨터정보학회논문지
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• 제21권11호
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• pp.17-21
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• 2016

Heterogeneity assessment of tumor in oncology is important for diagnosis of cancer and therapy. The aim of this study was performed assess heterogeneity tumor region in PET image using texture analysis. For assessment of heterogeneity tumor in PET image, we inserted sphere phantom in torso phantom. Cu-64 labeled radioisotope was administrated by 156.84 MBq in torso phantom. PET/CT image was acquired by PET/CT scanner (Discovery 710, GE Healthcare, Milwaukee, WI). The texture analysis of PET images was calculated using occurrence probability of gray level co-occurrence matrix. Energy and entropy is one of results of texture analysis. We performed the texture analysis in tumor, liver, and background. Assessment textural features of region-of-interest (ROI) in torso phantom used in-house software. We calculated the textural features of torso phantom in PET image using texture analysis. Calculated entropy in tumor, liver, and background were 5.322, 7.639, and 7.818. The further study will perform assessment of heterogeneity using clinical tumor PET image.

• 한국응급구조학회지
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• 제18권3호
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• pp.63-75
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• 2014

Purpose: The purpose of this study was to investigate the image about emergency medical technology (EMT) jobs and to identify factors influencing the image of EMT jobs among students of this department. Methods: A self-reported questionnaire was administered to 532 paramedic students in the cities of D, G, and J between May 28 and June 19, 2013. Data were analyzed by using the SPSS version 21.0 program. Results: The image about EMT jobs was positively related to self-esteem. However, the image about EMT jobs was negatively related to grade and hospital practice experience. In the multiple regression analysis, the adjusted $R^2$ value was .220 (p < .001). Conclusion: The importance of enhancing the self-esteem of paramedic students should be emphasized. Further research on the image about EMT jobs in the hospital practice setting is needed.

• 대한세포병리학회지
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• 제6권1호
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• pp.10-17
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• 1995

Anaplastic carcinoma of the thyroid gland is one of the most malignant tumors. Recently, DNA ploidy measured by flow cytometry and image analysis has been suggested as an additional useful indicator of tumor behavior. Studies on the occurrence and clinical significance of DNA aneuploidy in anaplastic carcinoma of the thyroid are rare. In this study, the pattern of DNA ploidy was measured by image analysis on Papanicolaou stained slides in four cases of anaplastic carcinoma and also measured by flow cytometry using paraffin blocks in two cases. In all cases of anaplastic carcinoma, DNA aneuploidy was found by image analaysis. By flow cytometry, one case had a diploid peak and the other case had an aneuploid peak. According to the above results, we conclude that anaplastic carcinoma of the thyroid glands have a high incidence of DNA aneuploidy and image analysis using Papanicolaou stained slides is a useful method in detecting DNA aneuploidy.

• 한국의학물리학회:학술대회논문집
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• 한국의학물리학회 2002년도 Proceedings
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• pp.322-323
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• 2002

Partial volume averaging effect of PET data influences on the accuracy of quantitative measurements of regional brain metabolism because spatial resolution of PET is limited. The purpose of this study was to evaluate the accuracy of partial volume correction carried out on $^{18}$ F-PET images using Hoffman brain phantom. $^{18}$ F-PET Hoffman phantom images were co-registered to MR slices of the same phantom. All the MR slices of the phantom were then segmented to be binary images. Each of these binary images was convolved in 2 dimensions with the spatial resolution of the PET. The original PET images were then divided by the smoothed binary images in slice-by-slice, voxel-by-voxel basis resulting in larger PET image volume in size. This enlarged partial volume corrected PET image volume was multiplied by original binary image volume to exclude extracortical region. The evaluation of partial volume corrected PET image volume was performed by region of interests (ROI) analysis applying ROIs, which were drawn on cortical regions of the original MR image slices, to corrected and original PET image volume. From the ROI analysis, range of regional mean values increases of partial volume corrected PET images was 4 to 14%, and average increase for all the ROIs was about 10% in this phantom study. Hoffman brain phantom study was useful for the objective evaluation of the partial volume correction method. This MR-based correction method would be applicable to patients in the. quantitative analysis of FDG-PET studies.

• 한국멀티미디어학회논문지
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• 제14권11호
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• pp.1383-1391
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• 2011

향상된 기능을 가진 최신 의료장비들의 등장으로 하드웨어 성능에 부합하는 효과적인 영상처리 및 분석의 중요성이 부각되고 있으며, 2차원 의료 영상처리 및 3차원 영상 재구성에 관한 많은 연구들이 진행되고 있다. 본 논문은 흉부 CT 영상을 사용하여 신체 장기를 단계별로 분할 하였으며, 분할된 결과 영상을 3차원으로 재구성 하였다. 다양한 영상분할 방법중 영역 확장법 및 효과적인 분할을 위해 선명화와 감마 조절등과 같은 영상 향상 기법을 적용하였으며, 기관지를 포함한 폐, 기관지, 폐 등의 순서로 영상을 분할하였다. 분할된 신체 장기 영상을 VTK를 사용하여 3차원 영상으로 재구성 하였으며, 병변 진단을 위한 2차원 및 3차원 의료 영상 처리와 분석에 활용될 것으로 판단된다.