• Transactions on Electrical and Electronic Materials
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• 제15권4호
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• pp.230-234
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• 2014

Automatic detection of disease helps medical institutions that are introducing digital images to read images rapidly and accurately, and is thus applicable to lesion diagnosis and treatment. The aim of this study was to apply a symmetry contribution algorithm to unsharp mask filter-applied MR images and propose an analysis technique to automatically recognize brain tumor and edema. We extracted the skull region and drawed outline of the skull in database of images obtained at P University Hospital and detected an axis of symmetry with cerebral characteristics. A symmetry contribution algorithm was then applied to the images around the axis of symmetry to observe intensity changes in pixels and detect disease areas. When we did not use the unsharp mask filter, a brain tumor was detected in 60 of a total of 95 MR images. The disease detection rate for the brain was 63.16%. However, when we used the unsharp mask filter, the tumor was detected in 87 of a total of 95 MR images, with a disease detection rate of 91.58%. When the unsharp mask filter was used in the pre-process stage, the disease detection rate for the brain was higher than when it was not used. We confirmed that unsharp mask filter can be used to rapidly and accurately to read many MR images stored in a database.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2402-2406
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• 2005

A collision query determines the intersection between given objects, and is used in computer-aided design and manufacturing, animation and simulation systems, and physically-based modeling. Bounding volume hierarchies are one of the simplest and most widely used data structures for performing collision detection on complex models. In this paper, we present hierarchy of oriented rounded bounding volume for fast proximity queries. Designing hierarchies of new bounding volumes, we use to combine multiple bounding volume types in a single hierarchy. The new bounding volume corresponds to geometric shape composed of a core primitive shape grown outward by some offset such as the Minkowski sum of rectangular box and a sphere shape. In the experiment of parallel close proximity, a number of benchmarks to measure the performance of the new bounding box and compare to that of other bounding volumes.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2482-2484
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• 2000

In this paper, we have developed an automated computer aided diagnostic (CAD) scheme by using artificial neural networks(ANN) on guantitative analysis of chest photofluorography. The first ANN performs the detection of suspicious regions in a low resolution image. This was trained specifically on the problem of detecting abnormal regions digitized chest photofluorography. The second space matching method was used to distinguish between normal and abnormal regions of interest(ROI). If the ratio of the number of abnormal ROI to the total number of all ROI in a chest image was greater than a specified threshold level, the image was classified as abnormal.

• 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.

• Journal of applied mathematics & informatics
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• 제14권1_2호
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• pp.461-471
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• 2004

In recent years, large volumes of data are transferred between a computer system and various subsystems through digital logic circuits and interconnected wires. And there always exist potential errors when data are transferred due to electrical noise, device malfunction, or even timing errors. In general, parity checking circuits are usually employed for detection of single-bit errors. However, it is not sufficient to enhance system reliability and availability for efficient error detection. It is necessary to detect and further correct errors up to a certain level within the affordable cost. In this paper, we report a generation of 3-distance code using the characteristic matrix of a PBCA.

• 대한임베디드공학회논문지
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• 제17권5호
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• pp.265-272
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• 2022

As the risk of lung cancer has increased, early-stage detection and treatment of cancers have received a lot of attention. Among various medical imaging approaches, computer tomography (CT) has been widely utilized to examine the size and growth rate of lung nodules. However, the process of manual examination is a time-consuming task, and it causes physical and mental fatigue for medical professionals. Recently, many computer-aided diagnostic methods have been proposed to reduce the workload of medical professionals. In recent studies, encoder-decoder architectures have shown reliable performances in medical image segmentation, and it is adopted to predict lesion candidates. However, localizing nodules in lung CT images is a challenging problem due to the extremely small sizes and unstructured shapes of nodules. To solve these problems, we utilize atrous spatial pyramid pooling (ASPP) to minimize the loss of information for a general U-Net baseline model to extract rich representations from various receptive fields. Moreover, we propose mixed-up attention mechanism of reverse, boundary and convolutional block attention module (CBAM) to improve the accuracy of segmentation small scale of various shapes. The performance of the proposed model is compared with several previous attention mechanisms on the LIDC-IDRI dataset, and experimental results demonstrate that reverse, boundary, and CBAM (RB-CBAM) are effective in the segmentation of small nodules.

• 한국국방경영분석학회지
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• 제23권2호
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• pp.120-134
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• 1997

This paper illustrates the process of developing and configuring the prototype computer-assisted analysis system named as Work-Expert for analyzing occupational stress. A Work-Expert was developed to allow the nonexperts or line manager to utilize the existing knowledge in the area of occupational stress estimation, and to provide intelligent and computer-aided problem solving. The purpose of the system development is for future prediction and problem solving. Creating preventive measures, such as early detection of stress, proper placement and promotion of employees, job enlargement, employee identification, employee involvement, communication, and training of managers will be possible by using this system effectively.

• 대한전자공학회논문지
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• 제19권4호
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• pp.26-30
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• 1982

PLA는 다입방-다출력에 적합하지만 입력변수의 증가와 적항의 증가에 따라 종래의 방법으로는 테스트 입력의 생성이 곤란하게 된다. 본 논문에서는 이와 같은 테스트 입방을 구하기 위하여 sharp 연산과 cap 연산을 동시에 처리하는 효과적인 컴퓨터 알고리듬을 구성하고 이것을 프로그램에 의해서 실증하였다.

• 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.