• Journal of the Korean Society of Radiology
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• v.18 no.5
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• pp.551-556
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• 2024

This study aims to evaluate the diagnostic concordance and accuracy by comparing thyroid lesions diagnosed with the artificial intelligence-based computer-aided diagnosis (CAD) system, S-Detect^TM, to the results of fine-needle aspiration biopsy(FNAB). A retrospective study was conducted involving 60 patients at N Hospital in Gyeongnam from May 2023 to September 2023. The study used S-Detect^TM to analyze ultrasound findings and malignancy risk of thyroid nodules and compared these findings with FNAB results to determine accuracy. The study assessed the sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of S-Detect^TM and evaluated the diagnostic concordance between the two methods using Kappa analysis. S-Detect^TM demonstrated a sensitivity of 90.5%, specificity of 83.2%, accuracy of 88.3%, PPV of 80.7%, and NPV of 92.7%. The Kappa value for diagnostic agreement between S-Detect^TM and FN AB was 0.719 (p<0.05), indicating a high level of agreement between the methods. Therefore, the CAD system S-Detect^TM proves valuable in distinguishing between malignant and benign thyroid lesions and could reduce unnecessary tissue examinations when used appropriately before thyroid fine-needle aspiration.

• Journal of the Korean Society of Radiology
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• v.10 no.2
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• pp.81-87
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• 2016

In this study we proposed a texture feature analysis algorithm that distinguishes between a normal image and a diseased image using CT images of some fatty liver patients, and generates both Eigen images and test images which can be applied to the proposed computer aided diagnosis system in order to perform a quantitative analysis for 6 parameters. And through the analysis, we derived and evaluated the recognition rate of CT images of fatty liver. As the results of examining over 30 example CT images of fatty liver, the recognition rates representing a specific texture feature-value are as follows: some appeared to be as high as 100% including Average Gray Level, Entropy 96.67%, Skewness 93.33%, and Smoothness while others showed a little low disease recognition rate: 83.33% for Uniformity 86.67% and for Average Contrast 80%. Consequently, based on this research result, if a software that enables a computer aided diagnosis system for medical images is developed, it will lead to the availability for the automatic detection of a diseased spot in CT images of fatty liver and quantitative analysis. And they can be used as computer aided diagnosis data, resulting in the increased accuracy and the shortened time in the stage of final reading.

• Journal of radiological science and technology
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• v.28 no.2
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• pp.123-128
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• 2005

It is necessary to obtain images of high diagnosis worth based on ability and skill of radiological technologist in case of acquisition of medical radiologaphy information. In addition, we need the various kinds of education considering individual radiological technologist variation and the organization of clinical education according to the large amount of learning and more expensive knowledge of radiology. In this paper, we implement CAI system for cervical vertebrae fracture radiograph with multimedia authoring tools on web environments. The CAI system can train a new radiological technologist and study a lot of radiological technologist on the cervical vertebrae fracture radiography acquisition based on more objective and systematical data in radiography room. The proposed CAI system is also expected as a useful program which can help to cure patients based on accurate diagnosis as well as obtain radiography more quickly using a technology of radiography acquisition for cervical vertebrae fracture.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.362-369
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• 2014

This study aimed to propose an accurate diagnostic method for osteoporosis by realizing a computer-aided diagnosis system with the application of the statistical analysis of texture features using digital images of lateral lumbar spine of patients with osteoporosis and providing reliable supplementary diagnostic information by model experimental research for early diagnosis of diseases. For these purposes, digital images of lateral lumbar spine of normal individuals and patients with osteoporosis were used in the experiments, and the values of statistical texture features on the set ROI were expressed in six parameters. Among the texture feature values of the six parameters of osteoporosis, the highest and lowest recognition rates of 95 and 80% were shown in average gray level and uniformity, respectively. Moreover, all the six parameters showed recognition rates of over 80% for osteoporosis: 82.5% in average contrast, 90% in smoothness, 87.5% in skewness, and 87.5% in entropy. Therefore, if a program developing into a computer-aided diagnosis system for medical images is coded based on the results of this study, it is considered possible to be applied to preliminary diagnostic data for automatic detection of lesions and disease diagnosis using medical images, to provide information for definite diagnosis of diseases, to diagnose by limited device, and to be used to shorten the time to analyze medical images.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.299-300
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• 2024

첨단 기술의 발전과 함께 지능형 운전자 보조 시스템의 성능 및 교통 시스템 체계가 고도화됨에 따라 전반적인 교통사고 발생 건수는 줄어드는 추세지만 대한민국의 교통사고 발생 빈도는 아직 OECD 평균 대비 높은 실정이다. 특히, 2020년 경제 협력 개발 기구(OECD) 통계에 따르면 대한민국의 인구 10만 명당 교통사고 사망자 수는 회원국 36개 중 29위로 매우 높은 축에 속한다. 따라서, 본 논문에서는 교통사고 발생률을 낮추는 데 도움을 줄 수 있는 주행 이상 패턴 감지 시스템을 제안한다. 제안한 방법에서는 실시간 영상 분석을 통해 신호등 및 차선을 인식함과 동시 차량 내부 진단 데이터에 대한 시계열 분석을 기반으로 운전자의 운전 패턴을 분석한 후 평소와 다른 이상 징후를 발견하면 운전자에게 경고 알림을 제공하여 위험한 상황을 회피할 수 있도록 지원한다.

• Annual Conference of KIPS
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• 2011.04a
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• pp.416-417
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• 2011

본 논문에서는 구조텐서와 모폴로지 연산을 이용한 유방 MR 영상에서 흉근을 제거하기 위한 분할 방법을 제안한다. 제안하는 방법은 영상의 그레디언트 정보를 나타내는 구조텐서와 복잡한 구조텐서를 평활화하기 위한 모폴로지 연산을 적용하여 영상 진단 및 영상 정합시 불필요한 흉근부분을 자동으로 분할하고자 한다. 실험결과에서 확인할 수 있듯이 정확한 분할의 결과는 향후 컴퓨터 보조 진단 시스템에 유용하게 사용할 수 있을 것으로 기대된다.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.562-569
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• 2022

The alarm monitoring technology applied to existing operating ships manages data items such as temperature and pressure with AMS (Alarm Monitoring System) and provides an alarm to the crew should these sensing data exceed the normal level range. In addition, the maintenance of existing ships follows the Planned Maintenance System (PMS). whereby the sensing data measured from the equipment is monitored and if it surpasses the set range, maintenance is performed through an alarm, or the corresponding part is replaced in advance after being used for a certain period of time regardless of whether the target device has a malfunction or not. To secure the reliability and operational safety of ship engine operation, it is necessary to enable advanced diagnosis and prediction based on real-time condition monitoring data. To do so, comprehensive measurement of actual ship data, creation of a database, and implementation of a condition diagnosis monitoring system for condition-based predictive maintenance of auxiliary equipment and piping must take place. Furthermore, the system should enable management of auxiliary equipment and piping status information based on a responsive web, and be optimized for screen and resolution so that it can be accessed and used by various mobile devices such as smartphones as well as for viewing on a PC on board. This update cost is low, and the management method is easy. In this paper, we propose CBM (Condition Based Management) technology, for autonomous ships. This core technology is used to identify abnormal phenomena through state diagnosis and monitoring of pumps and purifiers among ship auxiliary equipment, and seawater and steam pipes among pipes. It is intended to provide performance diagnosis and failure prediction of ship auxiliary equipment and piping for convergence analysis, and to support preventive maintenance decision-making.

• Journal of the Korean Society of Radiology
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• v.83 no.1
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• pp.149-161
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• 2022

Purpose To investigate the correlation between computer-aided diagnosis (CAD) parameters in 3-tesla (T) MRI and pathologic immunohistochemical (IHC) markers in invasive carcinoma of no special type (NST). Materials and Methods A total of 94 female who were diagnosed with NST carcinoma and underwent 3T MRI using CAD, from January 2018 to April 2019, were included. The relationship between angiovolume, curve peak, and early and late profiles of dynamic enhancement from CAD with pathologic IHC markers and molecular subtypes were retrospectively investigated using Dwass, Steel, Critchlow-Fligner multiple comparison analysis, and univariate binary logistic regression analysis. Results In NST carcinoma, a higher angiovolume was observed in tumors of higher nuclear and histologic grades and in lymph node (LN) (+), estrogen receptor (ER) (-), progesterone receptor (PR) (-), human epidermal growth factor 2 (HER2) (+), and Ki-67 (+) tumors. A high rate of delayed washout and a low rate of delayed persistence were observed in Ki-67 (+) tumors. In the binary logistic regression analysis of NST carcinoma, a high angiovolume was significantly associated with a high nuclear and histologic grade, LN (+), ER (-), PR (-), HER2 (+) status, and non-luminal subtypes. A high rate of washout and a low rate of persistence were also significantly correlated with the Ki-67 (+) status. Conclusion Angiovolume and delayed washout/persistent rate from CAD parameters in contrast enhanced breast MRI correlated with predictive IHC markers. These results suggest that CAD parameters could be used as clinical prognostic, predictive factors.

• Annual Conference of KIPS
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• 2009.04a
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• pp.43-46
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• 2009

의료 분야의 정보화 움직임으로 인해 단순한 정보 저장과 검색 시스템에서 벗어나 지능화된 서비스를 제공 해주는 시멘틱 웹 기반의 의료 시스템이 요구되고 있다. 이에 한의학 분야도 한의사의 진단을 보조할 수 있는 지식 기반 시스템에 대한 요구가 증대되고 있으며 관련 시스템들이 개발되어지고 있다. 온톨로지는 시멘틱 웹의 핵심적인 지식 체계로 지식의 처리와 추론이 가능하므로 질의 및 논리 추론을 통하여 진단을 내리는 한의학 지식 데이터베이스 구축에 적합하다. 본 연구에서는 한의학 분야의 지식을 보다 의미적이고 체계적으로 표현하고 온톨로지를 이용한 검색 결과의 이점을 보여주기 위해 추론 기술을 접목시켜 한의학 온톨로지를 개발하였다.

• Journal of the Korean Society of Radiology
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• v.9 no.6
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• pp.369-374
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• 2015

In this study we proposed a texture feature analysis algorithm that distinguishes between a normal image and a diseased image using CT images of some brain hemorrhage patients, and generates both Eigen images and test images which can be applied to the proposed computer aided diagnosis system in order to perform a quantitative analysis for 6 parameters. And through the analysis, we derived and evaluated the recognition rate of CT images of brain hemorrhage. As the results of examining over 40 example CT images of brain hemorrhage, the recognition rates representing a specific texture feature-value are as follows: some appeared to be as high as 100% including average gray level, average contrast, smoothness, and Skewness while others showed a little low disease recognition rate: 95% for uniformity and 87.5% for entropy. Consequently, based on this research result, if a software that enables a computer aided diagnosis system for medical images is developed, it will lead to the availability for the automatic detection of a diseased spot in CT images of brain hemorrhage and quantitative analysis. And they can be used as computer aided diagnosis data, resulting in the increased accuracy and the shortened time in the stage of final reading.