• Journal of the Korean Society of Visualization
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• v.5 no.2
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• pp.33-38
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• 2007

Water management in polymer electrolyte fuel cell (PEFC) has been receiving large attention as an important issue in practical applications. Proper water management is vital to achieve high performance and durability of PEFC. In this study, an X-ray imaging technique was employed to visualize the water distribution in a PEFC quantitatively. X-ray images of the PEFC components with and without water were distinguished clearly. From the visualized X-ray images, we could evaluate the water distribution in the region between separator and gas diffusion layer (GDL) quantitatively. In addition, the contact angle of water in the micro-channels was also clearly visualized.

• Journal of Korea Multimedia Society
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• v.8 no.4
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• pp.482-491
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• 2005

In this paper, we proposed the microcalcification detection algorithm which is based on wavelet transform and automatic thresholding method in the X-ray mammographic images. Digital X-ray imaging system is essential equipment in the field diagnosis and is widely used in the various fields such as chest, fracture of a bone, and dental correction. Especially, digital X-ray mammographic imaging is known as the most important method to diagnose the breast cancer, many researches to develop the imaging system are processing in country. In this paper, we proposed a microcalcifications detection algorithm necessary in the early phase of breast cancer diagnosis and showed that a algorithm could effectively detect microcalfication and could aid diagnosis-radiologist.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.614-617
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• 2022

인공지능의 발전으로 의료영상 분야에서 딥러닝 기반 질병 진단 연구가 활발하다. 그러나 모델 개발 시 학습 데이터의 개수와 품질은 매우 중요한데, 의료 분야 특성상 접근 가능한 데이터셋이 적으며 오픈 데이터셋은 서로 다른 기관에서 배포되거나 웹상에서 수집된 것으로 진단에 적합한 품질을 기대하기 어렵다. 또한, 기존 연구는 데이터셋이 학습에 적합한지에 대한 품질검증 없이 사용한다. 따라서 본 논문에서는 임상에서 사용하는 화질 평가 요소에 근거를 두고 영역별 화소값 분석을 통한 흉부 X선 영상 품질 평가 기법을 제안한다. 오픈 데이터셋 JSRT, Chest14와 국내 A 병원 데이터셋 AUH에 제안한 기법을 적용한 결과 민감도 91.5%, 특이도 96.1%의 우수한 성능을 확인하였다.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.789-795
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• 2020

Many deaths have been reported due to the worldwide pandemic of COVID-19. In order to prevent the further spread of COVID-19, it is necessary to quickly and accurately read images of suspected patients and take appropriate measures. To this end, this paper introduces a deep learning-based COVID-19 chest X-ray reading technique that can assist in image reading by providing medical staff whether a patient is infected. First of all, in order to learn the reading model, a sufficient dataset must be secured, but the currently provided COVID-19 open dataset does not have enough image data to ensure the accuracy of learning. Therefore, we solved the image data number imbalance problem that degrades AI learning performance by using a Stacked Generative Adversarial Network(StackGAN++). Next, the DenseNet-based classification model was trained using the augmented data set to develop the reading model. This classification model is a model for binary classification of normal chest X-ray and COVID-19 chest X-ray, and the performance of the model was evaluated using part of the actual image data as test data. Finally, the reliability of the model was secured by presenting the basis for judging the presence or absence of disease in the input image using Grad-CAM, one of the explainable artificial intelligence called XAI.

• 한국지구물리탐사학회:학술대회논문집
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• 2009.10a
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• pp.147-154
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• 2009

Physical properties of rocks, such as velocity, are strongly dependant on detailed pore structures, and recently, pore micro-structures by X-ray tomography techniques have been used to simulate and understand the physical properties. However, the smoothing effect during the tomographic reconstruction procedure often causes an artifact - overestimating the contact areas between grains. The pore nodes near a grain contact are affected by neighboring grain nodes, and are classified into grain nodes. By this artifact, the pore structure has higher contact areas between grains and thus higher velocity estimation than the true one. To reduce this artifact, we tried two image processing techniques - sharpening filter and neural network classification. Both methods gave noticeable improvement on contact areas between grains visually; however, the estimated velocities showed only incremental improvement. We then tried to change the resolutions of tomogram and quantify its impact on velocity estimation. The estimated velocity from the tomogram with higher spatial resolution was improved significantly, and with around 2 micron spatial resolution, the calculated velocity was very close to the lab measurement. In conclusion, the resolution of pore micro-structure is the most important parameter for accurate estimation of velocity using pore-scale simulation techniques. Also the estimation can be incrementally improved if combined with image processing techniques during the pore-grain classification.

• Journal of the Korea Computer Industry Society
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• v.3 no.9
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• pp.1279-1286
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• 2002

Generally, radiologists can fail to detect pulmonary nodules in up to 30%. If an automatic system can inform the radiologists of thelocations of the doubtful nodules in the X-ray chest images, the frequency of mistakenly observed numbers of the nodules can be potentially reduced. This software is using morphological filtering and two feature-extraction techniques. The morphological filtering is the first process, which subsequently adds the operations of erosion and dilation to the original images so that this process can transform the original X-ray chest images into manageable ones. The false-positives are frequently being mistaken as nodules but actually these are not real nodules. The second process is the two feature-extraction techniques which are used to reduce the false-positives. Therefore, this system will make more effective detection of pulmonary nodules by reducing the false-positives when applied to the X-ray chest images which is difficult to get accurate detection.

• Journal of Biomedical Engineering Research
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• v.21 no.5
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• pp.527-535
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• 2000

For a reliable patient set-up verification, better portal films are needed to track relevant features. Simulator films are compared with portal films as a reference image in radiotherapy planning. This shows some possibilities of the use of image information of simulator images for enhancement and restorations of portal images which are very poor in quality compared with the simulator images. This paper present an approach that combine an associative memory, a kind of artificial neural networks with fuzzy image enhancement technique using genetic algorithm which determines the fuzzy region of membership function by the use of maximum entropy principles. A higher portal image quality than conventional technique is achieved.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2577-2579
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• 2004

중성자를 사용한 비파괴검사는 X선을 사용하는 것에 비해 상대적으로 뛰어난 투과력을 가지고 있다. 하지만 중성자와 원자핵의 반응에 의한 scattering 효과와 중성자 빔의 uniformity부족 등으로 인한 영상의 왜곡이 발생한다. 본 논문에서는 이런 중성자 영상의 왜곡을 보정하기 위한 영상처리 알고리즘을 연구하고 연구된 알고리즘을 토대로 영상처리 소프트웨어를 구현하였다. 먼저 히스토그램 연산을 이용하여 영상의 밝기와 대비를 조절하여 영상의 가시성을 높였고, 필터링 기법을 통하여 영상이 가지는 임펄스 잡음과 가우시안 잡음을 순차적으로 제거하였다. 마지막으로 가우시안 잡음 제거시 부가적으로 발생한 영상의 흐려짐을 보완하여 보다 향상된 질의 영상을 얻게 되었다. 또한 Visual C++을 사용하여 위의 알고리즘들을 GUI 환경의 프로그램으로 구현하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04a
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• pp.767-770
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• 2002

일반적으로 방사선 의사들(radialogists)이 폐 노쥴(pulmonary nodule)을 탐지하는 데는 실제적으로 30%의 실패율을 가진다고 알려져 있다. 만약 자동화된 시스템이 체스트 영상에서 의심스런 노쥴들의 위치들을 방사선 의사에게 알려줄 수 있다면 잘못 판단되는 노쥴들의 수를 잠재적으로 줄일 수 있다. 우리는 형태학적 필터들(morphological filters)과 두가지 특징-추출(feature-extraction) 기술들을 포함하는 컴퓨터 자동 처리 시스템을 구현하였다. 본 시스템에서는 첫째로 형태학적 필터(morphological filtering) 처리를 행한다. 이 과정은 원래의 영상에 침식(erosion)과 확장 (dilation)을 연이어서 행하는 것으로 처리가 어려운 X 선 영상을 좀 더 다루기 쉬운 상태로 바꿔주는 역할을 하게 된다. 둘째는 일차적으로 노쥴로서 컴퓨터에 선택된 의심 부분에 가해주는 특징-추출 테스트로서 이 작용은 노쥴로 감지되었으나 실제로는 노쥴이 아닌 경우인 false-positive 갑지들을 줄이기 위해서 사용된다. 그리하여 본 시스템은 노쥴의 정확한 판독이 어려운 폐의 X 선 영상에 적용되어 false-positive 들을 효과적으로 줄임으로써 보다 효율적인 폐 노쥴의 탐지를 가능하게 하였다.

• Journal of Sensor Science and Technology
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• v.11 no.5
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• pp.263-272
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• 2002

In this paper, we investigate the potential for retrieval of morphometric data from three dimensional images of conducting bronchus obtained by X-ray Computerized Tomography (CT) and to explore the potential for the use of rapid prototype machine to produce physical hollow bronchus casts for mathematical modeling and experimental verification of particle deposition models. We segment the bronchus of lung by mathematical morphology method from obtained images by CT. The surface data representing volumetric bronchus data in three dimensions are converted to STL(streolithography) file and three dimensional solid model is created by using input STL file and rapid prototype machine. Two physical hollow cast models are created from the CT images of bronchial tree phantom and living human bronchus. We evaluate the usefulness of the rapid prototype model of bronchial tree by comparing diameters of the cross sectional area bronchus segments of the original CT images and the rapid prototyping-derived models imaged by X-ray CT.