• Korean Journal of Radiology
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• v.20 no.5
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• pp.749-758
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• 2019

Objective: To investigate whether a computer-aided diagnosis (CAD) system based on a deep learning framework (deep learning-based CAD) improves the diagnostic performance of radiologists in differentiating between malignant and benign masses on breast ultrasound (US). Materials and Methods: B-mode US images were prospectively obtained for 253 breast masses (173 benign, 80 malignant) in 226 consecutive patients. Breast mass US findings were retrospectively analyzed by deep learning-based CAD and four radiologists. In predicting malignancy, the CAD results were dichotomized (possibly benign vs. possibly malignant). The radiologists independently assessed Breast Imaging Reporting and Data System final assessments for two datasets (US images alone or with CAD). For each dataset, the radiologists' final assessments were classified as positive (category 4a or higher) and negative (category 3 or lower). The diagnostic performances of the radiologists for the two datasets (US alone vs. US with CAD) were compared Results: When the CAD results were added to the US images, the radiologists showed significant improvement in specificity (range of all radiologists for US alone vs. US with CAD: 72.8-92.5% vs. 82.1-93.1%; p < 0.001), accuracy (77.9-88.9% vs. 86.2-90.9%; p = 0.038), and positive predictive value (PPV) (60.2-83.3% vs. 70.4-85.2%; p = 0.001). However, there were no significant changes in sensitivity (81.3-88.8% vs. 86.3-95.0%; p = 0.120) and negative predictive value (91.4-93.5% vs. 92.9-97.3%; p = 0.259). Conclusion: Deep learning-based CAD could improve radiologists' diagnostic performance by increasing their specificity, accuracy, and PPV in differentiating between malignant and benign masses on breast US.

• Journal of Biomedical Engineering Research
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• v.30 no.2
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• pp.153-161
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• 2009

Mammogram is one of the important techniques for mass detection, which is the early diagnosis stage of a breast cancer. Especially, the CAD(Computer Aided Diagnosis) using mammogram improves the working performance of radiologists as it offers an effective mass detection. There are two types of CAD systems using mammogram; automatic and semi-automatic CAD systems. However, the automatic segmentation is limited in performance due to the difficulty of obtaining an accurate segmentation since mass occurs in the dense areas of the breast tissue and has smoother boundaries. Semi-automatic CAD systems overcome these limitations, however, they also have problems including high FP (False Positive) rate and a large amount of training data required for training a classifier. The proposed system which overcomes the aforementioned problems to detect mass is composed of the suspected area selection, the level set segmentation and SVM (Support Vector Machine) classification. To assess the efficacy of the system, 60 test images from the FFDM (Full-Field Digital Mammography) are analyzed and compared with the previous semi-automatic system, which uses the ANN classifier. The experimental results of the proposed system indicate higher accuracy of detecting mass in comparison to the previous systems.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.703-715
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• 2017

Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray-level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.678-700
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• 2023

Diagnosing breast diseases using ultrasound (US) images remains challenging because it is time-consuming and requires expert radiologist knowledge. As a result, the diagnostic performance is significantly biased. To assist radiologists in this process, computer-aided diagnosis (CAD) systems have been developed and used in practice. This type of system is used not only to assist radiologists in examining breast ultrasound images (BUS) but also to ensure the effectiveness of the diagnostic process. In this study, we propose a new approach for breast lesion localization and segmentation using a multi-scale pyramid of the ultrasound image of a breast organ and a convolutional semantic segmentation network. Unlike previous studies that used only a deep detection/segmentation neural network on a single breast ultrasound image, we propose to use multiple images generated from an input image at different scales for the localization and segmentation process. By combining the localization/segmentation results obtained from the input image at different scales, the system performance was enhanced compared with that of the previous studies. The experimental results with two public datasets confirmed the effectiveness of the proposed approach by producing superior localization/segmentation results compared with those obtained in previous studies.

• The Journal of Korean Academy of Prosthodontics
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• v.62 no.2
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• pp.157-164
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• 2024

A variety of digital technologies are being used throughout the entire implant treatment process of diagnosis, surgery, impression, design, and fabrication of prostheses. In this case, using a digital surgical guide, sinus floor elevation was performed without complications, and the implants were placed in the planned position. After the healing period for osseointegration, CAD-CAM (Computer-aided design-Computer-aided manufacturing) customized abutments and provisional prostheses were delivered. While using the provisional prosthesis, occlusal change was observed. To transfer the intermaxillary relationship and abutment position that reflect occlusal change and axial displacement, double scanning and abutment-level digital impressions were taken. Abutment superimposition was used to capture the subgingival margin without gingival retraction. Then, the definitive prosthesis was designed and fabricated with digital system. We report a case applying digital system, to achieve the predictable result as well as the efficient treatment process from implant surgery to fabricating prosthesis in the posterior area.

• Journal of the Korean Society of Systems Engineering
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• v.17 no.2
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• pp.91-97
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• 2021

User interface for cancer classification system is a software application with clinician's friendly tools and functions to diagnose cancer from pathology images. Pathology evolved from manual diagnosis to computer-aided diagnosis with the help of Artificial Intelligence tools and algorithms. In this paper, we explained each block of the project life cycle for the implementation of automated breast cancer classification software using AI and machine learning algorithms to classify normal and invasive breast histology images. The system was designed to help the pathologists in an automatic and efficient diagnosis of breast cancer. To design the classification model, Hematoxylin and Eosin (H&E) stained breast histology images were obtained from the ICIAR Breast Cancer challenge. These images are stain normalized to minimize the error that can occur during model training due to pathological stains. The normalized dataset was fed into the ResNet-34 for the classification of normal and invasive breast cancer images. ResNet-34 gave 94% accuracy, 93% F Score, 95% of model Recall, and 91% precision.

• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.358-366
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• 2012

Cirrhosis is a consequence of chronic liver disease characterized by replacement of liver tissue by fibrosis, scar tissue and regenerative nodules leading to loss of liver function. Liver Cirrhosis is most commonly caused by alcoholism, hepatitis B and C, and fatty liver disease, but has many other possible causes. Some cases are idiopathic disease from unknown cause. Abdomen of liver Computed tomography(CT) is one of the primary imaging procedures for evaluating liver disease such as liver cirrhosis, Alcoholic liver disease(ALD), cancer, and interval changes because it is economical and easy to use. The purpose of this study is to detect technique for computer-aided diagnosis(CAD) to identify liver cirrhosis in abdomen CT. We experimented on the principal components analysis(PCA) algorithm in the other method and suggested texture information analysis(TIA). Forty clinical cases involving a total of 634 CT sectional images were used in this study. Liver cirrhosis was detected by PCA method(detection rate of 35%), and by TIA methods(detection rate of 100%-AGI, TM, MU, EN). Our present results show that our method can be regarded as a technique for CAD systems to detect liver cirrhosis in CT liver images.

• The Journal of Korean Academy of Prosthodontics
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• v.55 no.1
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• pp.79-87
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• 2017

The open bite malocclusion is a common clinical entity and has multifactorial causes. Development of effective treatment plan and management is dependent on proper diagnosis. The skeletal open bite patient requires a coordinated orthodontic and orthognathic surgical approach to achieve stable occlusion, acceptable esthetics, and improved function. But in case of open bite with severely decayed dentition, restoration in the entire dentition is necessary. Using the facial analysis and diagnostic wax-up, the most effective treatment was prosthetic rehabilitation. The provisional restorations were fabricated to satisfy esthetic and functional requirements, which result in the uniformly distributed occlusal force, anterior and canine guidance. The inter-arch relationship, labio-dental harmony, and the soft tissue aspect, which is important to estimate the longevity were evaluated. Definitive restorations of monolithic zirconia were made by replicating provisional restorations by using the latest CAD/CAM technology. They were delivered to the patient and clinical follow-up observation was satisfactory.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2352-2355
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• 2003

In recent yeas, studies on computer aided diagnosis (CAD) using image analysis on CT images have been conducted with respect to various diseases. Extracting ground-glass opacities (GGO) on lung CT images is one of such subjects, though it has not found an established method yet. If the region of ground-glass opacities is large on CT images, it can be detected without much difficulty. On the other hand, if the region is small, it is still difficult to find it exactly. In the latter case, increasing overlooking possibility cannot be avoided according to smaller size of the region. To solve this difficulty, this paper proposes an automatic technique for extracting ground-glass opacities on lung CT images employing some statistical parameters of a gray level histogram and a differential histogram. The proposed technique is applied to some lung CT images in the performed experiment. The results are shown with discussion on future work.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.595-598
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• 2004

현재 급속하게 이루어지고 있는 의료영상저장전송 시스템의 보급으로 컴퓨터를 이용한 의료영상분야의 발전이 가속도를 받고 있다. 그러나 업체마다 통신관련 프로토콜 적용에 다소 차이가 존재하기 때문에 이후 도입해야 하는 CAD(Computer Aided Diagnosis) 등 분야로의 확장에 문제가 있다. 본 연구에서는 의료영상저장전송 시스템을 확장하고자 하는 경우에 고려해야 할 사항들에 대해서 제안하고 이를 토대로 새로운 의료영상저장전송 시스템을 구축하였다.