• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.43-52
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• 2024

Medical imaginings assume a important part in the analysis of tumors and cerebrospinal fluid (CSF) leak. Magnetic resonance imaging (MRI) is an image segmentation technology, which shows an angular sectional perspective of the body which provides convenience to medical specialists to examine the patients. The images generated by MRI are detailed, which enable medical specialists to identify affected areas to help them diagnose disease. MRI imaging is usually a basic part of diagnostic and treatment. In this research, we propose new techniques using the 4D-MRI image segmentation process to detect the brain tumor in the skull. We identify the issues related to the quality of cerebrum disease images or CSF leakage (discover fluid inside the brain). The aim of this research is to construct a framework that can identify cancer-damaged areas to be isolated from non-tumor. We use 4D image light field segmentation, which is followed by MATLAB modeling techniques, and measure the size of brain-damaged cells deep inside CSF. Data is usually collected from the support vector machine (SVM) tool using MATLAB's included K-Nearest Neighbor (KNN) algorithm. We propose a 4D light field tool (LFT) modulation method that can be used for the light editing field application. Depending on the input of the user, an objective evaluation of each ray is evaluated using the KNN to maintain the 4D frequency (redundancy). These light fields' approaches can help increase the efficiency of device segmentation and light field composite pipeline editing, as they minimize boundary artefacts.

• 대한후두음성언어의학회지
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• 제16권1호
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• pp.19-22
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• 2005

Background and Objectives : While considerable progress has been made in enhancing the quality of laryngoscopy and image processing, the evaluation of laryngeal erythema is still based on the clinician's judgement. The purpose of this study is to quantitatively measure the degree of erythema and to examine the relationship with clinical grading. Materials and Methods : Color images of larynx from 100 subjects were captured from video-documented examinations of laryngoscopy. The amount of erythema within the digitized larynx image was quantified using software developed and was compared with a grading system (0 to 3 scale) based on visual inspection by 4 experienced clinicians. The results were compared by deriving Kappa, Kendall and Spearman statistic. Results : There was high intra-observer(R=0.402-0.755) and inter-observer correlation (R=0.789). Among parameters, the red composite value had most remarkable agreement with clinical grading(R=0.827). Conclusion : The result suggest that the computer based analysis of laryngeal erythema can provide quantiative data on degree of erythema and the basis for further development of an expert system.

• Korean Journal of Radiology
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• 제25권6호
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• pp.518-539
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• 2024

Coronary computed tomography angiography (CCTA) has emerged as a pivotal tool for diagnosing and risk-stratifying patients with suspected coronary artery disease (CAD). Recent advancements in image analysis and artificial intelligence (AI) techniques have enabled the comprehensive quantitative analysis of coronary atherosclerosis. Fully quantitative assessments of coronary stenosis and lumen attenuation have improved the accuracy of assessing stenosis severity and predicting hemodynamically significant lesions. In addition to stenosis evaluation, quantitative plaque analysis plays a crucial role in predicting and monitoring CAD progression. Studies have demonstrated that the quantitative assessment of plaque subtypes based on CT attenuation provides a nuanced understanding of plaque characteristics and their association with cardiovascular events. Quantitative analysis of serial CCTA scans offers a unique perspective on the impact of medical therapies on plaque modification. However, challenges such as time-intensive analyses and variability in software platforms still need to be addressed for broader clinical implementation. The paradigm of CCTA has shifted towards comprehensive quantitative plaque analysis facilitated by technological advancements. As these methods continue to evolve, their integration into routine clinical practice has the potential to enhance risk assessment and guide individualized patient management. This article reviews the evolving landscape of quantitative plaque analysis in CCTA and explores its applications and limitations.

• 한국의학물리학회:학술대회논문집
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• 한국의학물리학회 2002년도 Proceedings
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• pp.65-67
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• 2002

A PC based software, the RTP Research Tool Box (RTB), was developed for IMRT optimization research. The software was consisted of an image module, a beam registration module, a dose calculation module, a dose optimization module and a dose display module. The modules and the Graphical User Interface (GUI) were designed to easily amendable by negotiating the speed of performing tasks. Each module can be easily replaced to new functions for research purpose. IDL 5.5 (RSI, USA) language was used for this software. Five major modules enable one to perform the research on the dose calculation, on the dose optimization and on the objective function. The comparison of three cost functions, such as the uncomplicated tumor control probability (UTCP), the physical objective function and the pseudo-biological objective function, which was designed in this study, were performed with the RTB. The optimizations were compared to the simulated annealing and the gradient search optimization technique for all of the optimization objective functions. No significant differences were found among the objective functions with the dose gradient search technique. But the DVH analysis showed that the pseudo-biological objective function is superior to the physical objective function when with the simulated annealing for the optimization.

• 대한방사선기술학회지:방사선기술과학
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• 제43권3호
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• pp.195-208
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• 2020

Artificial intelligence(AI) is a field of computer science that is defined as allowing computers to imitate human intellectual behavior, even though AI's performance is to imitate humans. It is grafted across software-based fields with the advantages of high accuracy and speed of processing that surpasses humans. Indeed, the AI based technology has become a key technology in the medical field that will lead the development of medical image analysis. Therefore, this article introduces and discusses the concept of deep learning-based medical imaging analysis using the principle of algorithms for convolutional neural network(CNN) and back propagation. The research cases application of the AI based medical imaging analysis is used to classify the various disease(such as chest disease, coronary artery disease, and cerebrovascular disease), and the performance estimation comparing between AI based medical imaging classifier and human experts.

• 한국통신학회논문지
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• 제38C권9호
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• pp.802-812
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• 2013

캡슐 내시경 시스템은 최소 침습적 방법으로 의료진이 환자의 소화기관 검사를 수행할 수 있는 혁신적 기술로 최근 주목 받는 융합 기술이다. 일단 환자가 비타민 알약 크기의 캡슐을 삼키면, 캡슐 내 무선 또는 인체통신 모듈에 의해 전송된 영상정보를 바탕으로 의료진은 캡슐 내시경 진단 시스템을 통해 혈액 이상, 용종, 궤양, 크론병 등 다양한 질환을 진단할 수 있다. 캡슐 내시경은 기존의 케이블 내시경을 대신할 수 있는 혁신 기술이지만 정확한 병증 진단 및 분석 소요 시간 측면에서 단점을 가지고 있다. 즉, 캡슐 내시경은 캡슐내의 소형 카메라를 이용하여 6~12만 프레임의 방대한 양의 정지 영상을 촬영하기 때문에 촬영된 영상을 의료진이 확인하고 정확한 병변을 분석하는데 오랜 시간이 걸린다. 따라서 캡슐 내시경 시스템은 빠른 병변 진단을 위한 도구 및 정확한 병변 진단을 위한 의료 이미지 영상 처리가 필수 요소이다. 본 논문에서는 캡슐 내시경 시스템의 주요 제조사들을 중심으로 거의 알려져 있지 않은 소화기 영상의 빠른 병변 진단 도구에 대한 최근 경향과 정확한 병변 검출을 위한 의료 영상 처리 기술의 최신 동향을 분석하였다.

• Applied Microscopy
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• 제46권2호
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• pp.93-99
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• 2016

The thickness of specimen is an important factor in microscopic researches. Thicker specimen contains more information, but it is difficult to obtain well focused image with precise details due to optical limit of conventional microscope. Recently, a microscope unit that combines improved illumination system, which allows real time three-dimensional (3D) image and automatic z-stack merging software. In this research, we evaluated the usefulness of this unit in observing thick samples; Golgi stained nervous tissue and ground prepared bone, tooth, and non-transparent small sample; zebra fish teeth. Well focused image in thick samples was obtained by processing z-stack images with Panfocal software. A clear feature of neuronal dendrite branching pattern could be taken. 3D features were clearly observed by oblique illumination. Furthermore, 3D array and shape of zebra fish teeth was clearly distinguished. A novel combination of two channel oblique illumination and z-stack imaging process increased depth of field and optimized contrast, which has a potential to be further applied in the field of neuroscience, hard tissue biology, and analysis of small organic structures such as ear ossicles and zebra fish teeth.

• 한국컴퓨터그래픽스학회논문지
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• 제28권2호
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• pp.11-19
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• 2022

최근 의료영상의 발전에 따라 의료 영상 생성에 대한 다양한 연구가 제안되고 있는데, 이와 관련하여 생성된 의료 영상의 품질과 다양성을 정확하게 평가하는 것이 중요해지고 있다. 생성된 의료 영상을 평가하는 방법으로는 전문가의 시각적 튜링 테스트(visual turing test), 특징 분포 시각화, IS, FID를 통한 정량적 평가를 통해 평가하고 있으나 의료 영상을 품질(fidelity)과 다양성(diversity) 측면에서 정량적으로 평가 하는 방법은 거의 이루어지고 있지 않다. 본 논문에서는 DCGAN과 PGGAN 생성 모델을 통해 비소세포폐암 환자의 흉부 CT 데이터 셋을 학습하여 영상을 생성하고, 이를 품질(fidelity)과 다양성(diversity) 측면에서 두 생성 모델의 성능을 평가한다. 1차원 점수 기반 평가방법인 IS, FID와 2차원 점수 기반 평가방법인 Precision 및 Recall, 개선된 Precision 및 Recall을 통해 성능을 정량적으로 평가하고, 의료영상에서의 각 평가방법들의 특징과 한계점에 대해서도 분석한다.

• 대한방사성의약품학회지
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• 제9권1호
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• pp.3-8
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• 2023

Parkinson's disease is a neurodegenerative disease caused by damage to brain neurons related to dopamine. Non-clinical animal models mainly used in Parkinson's disease research include drug-induced models of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine and 6-hydroxydopamine, and genetically modified transgenic animal models. Parkinson's diagnosis can be made using brain imaging of the substantia nigra-striatal dopamine system and using a radiotracer that specifically binds to the dopamine transporter. In this study, ¹⁸F-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane was used to confirm the image evaluation cutoff between normal and parkinson's disease models, and to confirm model persistence over time. In addition, the efficacy of single or combined administration of clinically used therapeutic drugs in parkinson's animal models was evaluated. Image analysis was performed using the PMOD software. Converted to standardized uptake value, and analyzed by standardized uptake value ratio by dividing the average value of left striatum by the average value of right striatum obtained by applying positron emission tomography images to the atlas magnetic resonance template. The image cutoff of the normal and the parkinson's disease model was calculated as SUVR=0.829, and it was confirmed that it was maintained during the test period. In the three-drug combination administration group, the right and left striatum showed a high symmetry of more than 0.942 on average and recovered significantly. Images using ¹⁸F-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane are thought to be able to diagnose and evaluate treatment efficacy of non-clinical Parkinson's disease.

• 융합신호처리학회논문지
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• 제24권4호
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• pp.205-212
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• 2023

심장 질환 가운데에서 부정맥은 방치할 경우에 뇌졸중, 심장 마비, 심부전과 같은 심각한 합병증이 발생할 수 있기 때문에 지속적이고 정확한 심전도 관리에 의한 건강 상태의 확인은 임상적 치료에 매우 중요한 요소이다. 그러나, 심전도(Electrocardiogram; ECG) 데이터의 정확한 해석은 전적으로 의료 전문가에 의존하기 때문에 부가적인 시간과 비용을 요구한다. 따라서 본 논문에서는 라이프로그 기반의 비정상적인 맥파 파형의 분석을 통한 의료 플랫폼 개발을 목적으로 부정맥 인식 모듈을 제안한다. 제안하는 방법은 ECG 데이터를 시계열 데이터가 아닌 이미지 형식으로 처리하여 시각적 패턴 인식 기술을 적용한 후, CNN 모델을 이용하여 부정맥을 탐지하는 방법을 제안한다. 본 논문에서 제안한 ECG 데이터의 이미지 타입 변환에 의한 CNN 모델의 부정맥 분류의 유효성 검증하기 위해 MIT-BIH 부정맥 데이터셋을 사용한 결과, 97%의 정확도를 보였다.