• Korean Journal of Radiology
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• 제20권5호
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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.

• 대한의용생체공학회:의공학회지
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• 제30권2호
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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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• 제13권4호
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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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• 제17권3호
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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.

• 대한치과보철학회지
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• 제62권2호
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• pp.157-164
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• 2024

진단, 수술, 인상, 보철물 제작 등 임플란트 치료 전 과정에서 디지털 기술이 활용되고 있다. 본 증례에서는 디지털 수술 가이드를 이용하여 합병증 없이 상악동 거상술을 시행하고, 계획된 위치에 임플란트를 식립하였다. 골유착을 위한 치유 기간 이후, CAD-CAM(Computer-aided design/Computer-aided Manufacturing)으로 맞춤형 지대주 및 임시 보철물을 제작하여 장착하고, 환자의 적응도와 교합을 평가하였다. 임시 보철물상에서 교합 변화가 관찰되어, 광중합형 컴포지트 레진으로 수리하였다. 최종 보철물 제작 시, 지대주의 수직 침하, 임시 보철물의 형태와 적응된 교합 관계를 반영하기 위해, 이중 스캔과 지대주 수준의 디지털 인상을 채득하였다. 치은 압배 없이 치은 연하 변연을 인기하기 위해, CAD-CAM 소프트웨어상에서 라이브러리화된 지대주 데이터를 중첩하고, 지르코니아 최종보철물 제작하여 장착하였다. 구치부 임플란트 수복 시, 디지털 시스템을 이용하여 전통적인 방법에서 겪는 어려움을 줄이고, 수술부터 보철물 제작까지 효율적인 치료 과정과 안정적이고 예지성 있는 결과를 얻어 보고하는 바이다.

• 시스템엔지니어링학술지
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• 제17권2호
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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.

• 한국콘텐츠학회논문지
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• 제12권4호
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• pp.358-366
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• 2012

간경화(liver cirrhosis)는 섬유조직의 증식과 재생성 결절 형성의 형태학적인 변화로 2차적으로 간내혈관의 변형 및 간기능의 저하가 나타나는 질병이며, 정맥류, 복수와 부종, 간성뇌증, 간암 등의 합병증 동반을 미연에 방지하는 것이 간경변증 진단 및 치료에 핵심이다. 일반적으로 간 컴퓨터단층영상이 간경변의 진단 및 병기를 결정하는 방법으로 사용한다. 그러므로 본 연구에서는 간경화의 자동 인식을 위하여 PCA와 TIA 알고리즘을 이용한 특징추출을 통하여 간경변의 자동 검출능력을 알아보고, 각 알고리즘간의 성능을 비교하였다. 실험은 학습영상과 테스트영상으로 구분한다. 고유영상을 생성시키기 위한 학습영상으로 정상영상이 사용되고, 테스트영상으로는 간경화영상이 사용된다. 간 CT 영상에서 간의 질병 부위를 균등하게 ROI 설정하고, $50{\times}50$ 픽셀 크기로 영상을 저장하여 실험하였다. 실험결과로 PCA는 간경화 검출율이 35%로 질병 인식으로 부적합하며, TIA 알고리즘의 AGL, TM, MU, EN는 100% 질병 인식력을 나타내어 간경화 자동 진단 인식으로 가능했다. 또한 결과를 임상에 적용하여 간경변의 컴퓨터보조진단으로 활용한다면 영상의학과 의사에게 업무 부담을 줄이고, 일차적 간경변의 스크리닝 도구로서 활용이 가능할 것이다. 그리고 TIA 알고리즘을 활용한 자동진단은 질병 진단의 전단계로서 예비판독의 정보를 제공하며 간경변의 조기 진단 및 예방이 가능다고 판단된다.

• 대한치과보철학회지
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• 제55권1호
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• pp.79-87
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• 2017

전치부 개방 교합은 다양한 원인이 복합적으로 작용하고, 진단에 따라 효과적인 치료 계획과 적절한 유지 방법을 선택할 수 있다. 대부분의 경우 교정과 악교정 수술을 통해 안정적인 교합을 얻고, 기능과 심미를 회복한다. 하지만 전체 치열에서 심한 우식증이 있는 경우 광범위한 수복이 필요하게 되므로, 보철 수복을 통해서도 교합을 재형성할 수 있다. 본 증례는 심한 우식을 동반한 골격성 전치부 개방 교합 환자에서 안모 분석 및 진단 납형으로부터 가장 효과적인 치료로 전악 보철 수복을 선택하였다. 교합력을 균등하게 분산하고 심미적인 임시 수복물을 제작하였고, 악간 관계 평가, 혀 등 연조직의 적응, 입술과의 조화를 관찰하고 수정하였다. 충분한 기간 동안 사용한 임시 수복물을 CAD/CAM (Computer-aided design/computer-aided manufacturing)을 이용하여 단일구조 지르코니아 최종 보철물로 이행하였고, 치료 종결 후 3개월 간 주기적으로 관찰하였을 때 만족스러운 결과를 얻었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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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.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2004년도 추계학술발표논문집(상)
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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.