• 대한의용생체공학회:의공학회지
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• 제27권5호
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• pp.291-299
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• 2006

A Computer-Aided Diagnosis system has been examined to reduce the effort of radiologist. In this paper, we propose the algorithm using Support Vector Machine(SVM) classifier to discriminate whether microcalcifications are malignant or benign tumors. The proposed method to detect microcalcifications is composed of two detection steps each of which uses SVM classifier. The coarse detection step finds out pixels considered high contrasts comparing with neighboring pixels. Then, Region of Interest(ROI) is generated based on microcalcification characteristics. The fine detection step determines whether the found ROIs are microcalcifications or not by merging potential regions using obtained ROIs and SVM classifier. The proposed method is specified on Korean mammogram database. The experimental result of the proposed algorithm presents robustness in detecting microcalcifications than the previous method using Artificial Neural Network as classifier even when using small training data.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2010년도 추계학술발표대회
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• pp.672-675
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• 2010

초음파 영상은 다른 의료 진단 방법에 비해 상대적으로 비용이 적게 들고 데이터 획득이 용이하기 때문에 널리 이용되고 있다. 초음파 영상은 획득 방법에 따라 화질이 차이가 난다. 고식적 초음파 영상에 비해 두 배의 주파수를 사용하는 하모닉 영상은 대조도나 해상도가 향상되고, 영상 내 잡음이 감소한다. 그래서 초음파 영상을 이용한 진단 과정에서 병변의 특징을 육안으로 정확하게 관찰할 수 있고, 이를 통해서 진단 결과의 정확성이 향상된다. 본 논문에서는 초음파 영상의 획득 방법의 차이에 따른 진단 성능의 차이를 컴퓨터를 이용한 병변 분류 성능을 통해서 비교했다. 이를 위해서 초음파를 통해서 획득한 영상에서 병변의 형태 및 질감 특징을 추출하고, 이를 바탕으로 병변을 분류하는 시스템 구성하였다. 실험을 통해서 하모닉 초음파 영상을 이용한 컴퓨터 기반 분류 방법이 고식적 초음파를 이용한 방법에 비해서 6% 정확성 향상이 있는 것을 확인하였다.

• Journal of applied mathematics & informatics
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• 제41권6호
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• pp.1257-1274
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• 2023

Rotating machines heavily rely on an intricate network of interconnected sub-components, with bearing failures accounting for a substantial proportion (40% to 90%) of all such failures. To address this issue, intelligent algorithms have been developed to evaluate vibrational signals and accurately detect faults, thereby reducing the reliance on expert knowledge and lowering maintenance costs. Within the field of machine learning, Artificial Immune Systems (AIS) have exhibited notable potential, with applications ranging from malware detection in computer systems to fault detection in bearings, which is the primary focus of this study. In pursuit of this objective, we propose a novel procedure for detecting novel instances of anomalies in varying operating conditions, utilizing only the signals derived from the healthy state of the analyzed machine. Our approach incorporates AIS augmented by Dynamic Time Warping (DTW). The experimental outcomes demonstrate that the AIS-DTW method yields a considerable improvement in anomaly detection rates (up to 53.83%) compared to the conventional AIS. In summary, our findings indicate that our method represents a significant advancement in enhancing the resilience of AIS-based novelty detection, thereby bolstering the reliability of rotating machines and reducing the need for expertise in bearing fault detection.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.940-952
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• 2020

The Alzheimer's disease (AD) is a neurodegenerative disease commonly found in the elderly individuals. It is one of the most common forms of dementia; patients with AD suffer from a degradation of cognitive abilities over time. To correctly diagnose AD, compuated-aided system equipped with automatic classification algorithm is of great importance. In this paper, we propose a novel deep learning based classification algorithm that takes advantage of MRI biomarker images including brain areas of hippocampus and cerebrospinal fluid for the purpose of improving the AD classification performance. In particular, we develop a new approach that effectively applies MRI biomarker patch images as input to 3D Deep Convolution Neural Network. To integrate multiple classification results from multiple biomarker patch images, we proposed the effective confidence score fusion that combine classification scores generated from soft-max layer. Experimental results show that AD classification performance can be considerably enhanced by using our proposed approach. Compared to the conventional AD classification approach relying on entire MRI input, our proposed method can improve AD classification performance of up to 10.57% thanks to using biomarker patch images. Moreover, the proposed method can attain better or comparable AD classification performances, compared to state-of-the-art methods.

• 대한방사선기술학회지:방사선기술과학
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• 제26권3호
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• pp.13-17
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• 2003

현대 병원들은 보다 나은 의료서비스를 위해 디지털 시스템을 갖추고자 노력하고 있다. 하지만, 아직도 많은 부분은 아날로그 시스템과 Film 출력에 의존하고 있다. 본 연구는 차량 이동형 흉부 전용 간접 촬영기에 디지털 영상 변환 장치와 이에 연동되는 X-ray 발생장치의 제어 시스템, 출력 시스템을 디지털시스템으로 변환, 연동시켰으며, 획득한 영상을 간접 촬영 전용프로그램에서 편리하게 판독 할 수 있도록 설계하여 임상에 적용시켰다. 이러한 과정에서 발생되는 문제점을 현실적으로 해결하였으며, 방사선사 입장에서 업무의 효율성을 높이고자 몇 가지 프로그램을 개발 적용하였다. 향후 미래지향적인 디지털의료 영상 시스템을 갖추기 위해 각종 프로그램과 시스템과도 연동이 되도록 설계하여 임상에 적용하여 우수성을 입증하였다.

• Journal of Gastric Cancer
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• 제24권3호
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• pp.327-340
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• 2024

Purpose: Results of initial endoscopic biopsy of gastric lesions often differ from those of the final pathological diagnosis. We evaluated whether an artificial intelligence-based gastric lesion detection and diagnostic system, ENdoscopy as AI-powered Device Computer Aided Diagnosis for Gastroscopy (ENAD CAD-G), could reduce this discrepancy. Materials and Methods: We retrospectively collected 24,948 endoscopic images of early gastric cancers (EGCs), dysplasia, and benign lesions from 9,892 patients who underwent esophagogastroduodenoscopy between 2011 and 2021. The diagnostic performance of ENAD CAD-G was evaluated using the following real-world datasets: patients referred from community clinics with initial biopsy results of atypia (n=154), participants who underwent endoscopic resection for neoplasms (Internal video set, n=140), and participants who underwent endoscopy for screening or suspicion of gastric neoplasm referred from community clinics (External video set, n=296). Results: ENAD CAD-G classified the referred gastric lesions of atypia into EGC (accuracy, 82.47%; 95% confidence interval [CI], 76.46%-88.47%), dysplasia (88.31%; 83.24%-93.39%), and benign lesions (83.12%; 77.20%-89.03%). In the Internal video set, ENAD CAD-G identified dysplasia and EGC with diagnostic accuracies of 88.57% (95% CI, 83.30%-93.84%) and 91.43% (86.79%-96.07%), respectively, compared with an accuracy of 60.71% (52.62%-68.80%) for the initial biopsy results (P<0.001). In the External video set, ENAD CAD-G classified EGC, dysplasia, and benign lesions with diagnostic accuracies of 87.50% (83.73%-91.27%), 90.54% (87.21%-93.87%), and 88.85% (85.27%-92.44%), respectively. Conclusions: ENAD CAD-G is superior to initial biopsy for the detection and diagnosis of gastric lesions that require endoscopic resection. ENAD CAD-G can assist community endoscopists in identifying gastric lesions that require endoscopic resection.

• 한국멀티미디어학회논문지
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• 제9권12호
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• pp.1588-1595
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• 2006

In this paper, we proposed a liver extracting procedure for computer aided liver diagnosis system. Extraction of liver region in an abdominal CT image is difficult due to interferences of other organs. For this reason, liver region is extracted in a region of interest(ROI). ROI is selected by the window which can measure the distribution of Hounsfield Unit(HU) value of liver region in an abdominal CT image. The distribution is measured by an existential probability of HU value of lever region in the window. If the probability of any window is over 50%, the center point of the window would be assigned to ROI. Actually, liver region is not clearly discerned from the adjacent organs like muscle, spleen, and pancreas in an abdominal CT image. Liver region is extracted by the watershed segmentation algorithm which is effective in this situation. Because it is very sensitive to the slight valiance of contrast, it generally produces over segmentation regions. Therefore these regions are required to merge into the significant regions for optimal segmentation. Finally, a liver region can be selected and extracted by prier information based on anatomic information.

• 대한의용생체공학회:의공학회지
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• 제38권5호
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• pp.237-241
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• 2017

Early detection of the pulmonary nodule is important for diagnosis and treatment of lung cancer. Recently, CT has been used as a screening tool for lung nodule detection. And, it has been reported that computer aided detection(CAD) systems can improve the accuracy of the radiologist in detection nodules on CT scan. The previous study has been proposed a method using Convolutional Neural Network(CNN) in Lung CAD system. But the proposed model has a limitation in accuracy due to its sparse layer structure. Therefore, we propose a Deep Convolutional Neural Network to overcome this limitation. The model proposed in this work is consist of 14 layers including 8 convolutional layers and 4 fully connected layers. The CNN model is trained and tested with 61,404 regions-of-interest (ROIs) patches of lung image including 39,760 nodules and 21,644 non-nodules extracted from the Lung Image Database Consortium(LIDC) dataset. We could obtain the classification accuracy of 91.79% with the CNN model presented in this work. To prevent overfitting, we trained the model with Augmented Dataset and regularization term in the cost function. With L1, L2 regularization at Training process, we obtained 92.39%, 92.52% of accuracy respectively. And we obtained 93.52% with data augmentation. In conclusion, we could obtain the accuracy of 93.75% with L2 Regularization and Data Augmentation.

• 인지과학
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• 제9권1호
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• pp.31-39
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• 1998

본 논문은 HELPS라는 반도체 검사 장비를 위한 전자성능지원시스템(Electronic Performance Support System)에 대하여 기술한다. 본 시스템의 목적은 운영 지원, 훈련, 지식 기반 고장 진단 및 수리를 위한 just-in time. on-the-job 멀티미디어에 기반을 둔 정보를 제공함으로써 오퍼레이터의 생산성을 향상시키는데 있다. HELPS는 운영 모듈과 고장 진단 모듈로 구성되어 있다. 운영 모듈은 사용자에게 장비에 관한 자세하고 쉽게 접근이 가능한 정보를 제공하기 위하여 멀티미디어와 하이퍼미디어를 사용한다. 멀티미디어는 정지 화상, 동화상, 에니메이션, 텍스트, 그래픽, 오디오를 포함하는 다매체 형식을 통합한다. 하이 퍼미디어는 계층적인 정보 구조를 통하여 숙련된 오퍼레이터에게는 작업을 수행하는데 필요한 특정한 정보만을 제공할 뿐만 아니라, 초보 오퍼레이터에게는 자세한 시스템 안내와 정보를 제공한다. 고장 진단 모듈은 오퍼레이터가 장비의 고장을 진단하고 수리하는 것을 자원하기 위하여 전문가 시스템과 멀티미디어를 통합하여 구성하였다. 전문가 시스템을 사용하여 진단한 다음, 멀티미디어에 의한 어드바이스가 텍스트를 포함한 정지 화상이나 음성을 포함한 동화상에 의해 제시된다. HELPS는 훈련 시간과 고장 진단 및 수리 시간의 측면에서 평가되었고, 본 시스템 사용 전에 비하여 약 30%이상의 시간을 절감함으로써 생산성 향상에 크게 도움이 된다는 것을 확인하였다. 앞으로 본 시스템은 ICAI와 가상현실 기법 등을 이용하여 확장된다면 더 많은 생산성 향상을 기대할 수 있을 것이다.