• International Journal of Advanced Culture Technology
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• v.11 no.1
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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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.1
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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.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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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.

• Journal of Biomedical Engineering Research
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• v.15 no.1
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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).

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.191-199
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• 2009

The integrated medical information system is an effective medical diagnosis assistance system which offers an environment in which medial images and diagnosis information can be shared. Because of the large-scale medical institutions and their cooperating organizations are operating the integrated medical information systems, they can share medical images and diagnosis information. However, this system can only stored and transmitted information without other functions. To resolve this problem and to enhance the efficiency of diagnostic activities, a medical image analysis system is necessary. In this paper, the proposed relationship method analyzes medical images for features generation. Under this method, the medical images have been segmented into several objects. The medical image features have been extracted from each segmented image. Then, extracted features were applied to the Relationship Method for medical image analysis. Several experimental results that show the effectiveness of the proposed method are also presented.

• Journal of the Korea Society of Computer and Information
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• v.21 no.11
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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.

• The Korean Journal of Emergency Medical Services
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• v.18 no.3
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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.

• The Korean Journal of Cytopathology
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• v.6 no.1
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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.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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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.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1383-1391
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• 2011

Following the appearance of the latest medical equipment with improved function, the importance of image analysis which enables effective image processing and analysis consistent with the hardware performance is on the rise. As well as, ongoing study is being done on the 2D medical image processing and 3D reconstruction. This paper segments chest CT images into each stage and finally shows 3D reconstruction of each segmented result. Among various image segmentation methods, Region Growing and apply sharpening and Gamma Controller as for image improvement for effective segmentation, image segmentation in order of bronchus and lung, bronchus, lung. Human organs image of segmented is use VTK(Visualization Toolkit) to make 3D reconstruction, two and three-dimensional medical image processing and analysis for lesions diagnosis are able to utilized.