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To segment magnetic resonance image series is an interdisciplinary topic that involves both medical and computer science. It is one of the most important steps for medical diagnosis and quantitative analysis. This paper proposes an automatic segmentation method based on support vector machine (SVM). Feature vectors are generated according to both grayscale value and texture pattern of MR brain images...
Watershed transformation is a common technique for image segmentation. However, its use for medical image segmentation has been limited particularly due to over-segmentation. In response to the characteristics of medical image, especially the contour extraction from the MRI (magnetic resonance imaging) brain image, this paper proposes an improved method in order to overcome the drawbacks. Firstly,...
For feature extraction in medical images, the scale-rate is proposed to represent local spatial or temporal complexity in medical images. The scale-rate is applied in automatic echocardiogram analysis. The experimental results show that the proposed method is effective and has promising application in automatic echocardiogram segmentation. Moreover, the directional spatial distribution of the scale-rate...
The vascular geometry may play an important role in the development of atherosclerosis by modulating the local hemodynamics and mechanical stresses of the vessel wall. The mouse is now the most popular animal model to study cardiovascular disease. Here, we present a method to characterize the 3-D geometry of mouse aortic arches by casting and light stereo-microscopic imaging. After calibration of...
In this study, we propose a method to estimate arterial wall strain using intravascular ultrasound (IVUS) images. The method is based on a nonrigid image registration algorithm, which represents the displacement field by cubic B-splines, and incorporates smoothness and incompressibility constraints. The 2D displacement field is then used to calculate the local strain tensors. With the 2D strain tensors,...
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