• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.10
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• pp.1294-1304
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• 1995

In this paper, a method has been proposed for the fully automatic detection of left ventricular endocardial boundary in B-mode short axis echocardiography without manual intervention by human operator. The proposed method makes use of the weighted model that approximates to endocardium and incomplete edge information for echocardiography. Therefore, this method is more effective than boundary detection by only edge information. The implementation of this method is as follows. First, the proposed algorithms are used in order to detect the approximate boundary, then a weighted model with the approximate boundary is constructed. Finally, the cavity center of the left ventricle performing the Hough transform with the weighted model and edge image can be found automatically, and then the endocardial boundary using detected center, original image, weighted model, and edge image can be detected. validations of this method with experimental results on echo image of dog's heart and clinical echocardiography is verified.

• Journal of the Optical Society of Korea
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• v.10 no.1
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• pp.42-47
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• 2006

M-mode imaging of the in vivo murine myocardium using optical coherence tomography (OCT) is described. Application of conventional techniques (e.g. MRI, Ultrasound imaging) for imaging the murine myocardium is problematic because the wall thickness is less than 1.5 mm (20 g mouse), and the heart rate can be as high as six hundred beats per minute. To acquire a real-time image of the murine myocardium, OCT can provide sufficient spatial resolution ($10{\mu}m$) and imaging speed (1000 A-scans/s). Strong light scattering by blood in the heart causes significant light attenuation, which makes delineation of the endocardium-chamber boundary problematic. To measure the thickness change of the myocardium during one heart beat cycle, a myocardium edge detection algorithm is developed and demonstrated.

• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.325-332
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• 1994

In this paper, a method is proposed to detect the endocardial contour of the left ventricle using the Hough transform with a weighted model and edge information from the 2D echocardiogram. The implementation of this method is as follows: first, an approximate model detection algorithm was implemented in order to detect the approximate endocardium model and the model center, then we constructed a weighted model with the detected model. Next, we found automatically the cavity center of the left ventricle performing the Hough transform which used the weighted model, and then we detected the endocardial contour using weighted model and edge image.

• Progress in Medical Physics
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• v.10 no.3
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• pp.115-123
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• 1999

The two dimensional echocardiography is widely used to evaluate regional wall motion abnormality, because of its abilities to depict left ventricular wall motion. A number of researches have been processed for evaluation and quantitative analysis of left ventricular wall motion functions. In this paper, we proposed an algorithm which detects automatically and analyze quantitatively endocardial wall motion during systole. The echocardiograms were obtained in the short-axis views in normal subjects. Automated edge detection and endocardial contour tracking algorithm was applied to each frames, quantitative analysis based on segmentation was performed, pre-defined color overlays superimposed on the gray scale images, and the images was animated. The proposed algorithm provided automated, quantitative diagnosis of regional wall motion abnormality.