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30 April 2004 Quantifying vascular structures in MRA images using hybrid PDE and geometric deformable models
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Segmentation and measurement of vascular structures is an important topic in medical image analysis. The aim of this paper is to present a hybrid approach to accurate segmentation of vascular structures from MRA images using level set methods and deformable geometric model, constructed with 3D Delaunay triangulation. Based on the analysis of local intensity structure, multiple scale filtering is derived from the Hessian matrix and then is used to effectively enhance vessel structures with various diameters. We apply the level set method to automatically segment vessels enhanced by the filtering. The segmentation of vessels from 3D vessel enhanced images can be regarded as an evolution of a propagating implicit surface in a 3D space that separates vessel volumes from another and moves in a normal direction to the vessel boundaries with a given speed function over time. The speed function used is derived from the results of filtering. In subsequent step, in order to make the segmented vessel surface fit the actual vessel surface more accurately, we triangulate the segmented vessel surface using 3D Delaunay triangulation and use the triangulated surface as a deformable model in order to minimize an energy functional in which the internal force is defined as linear equations with the local surface patch given by 3D Delaunay triangulation and the external force is derived from the gradient information of original images. Using the proposed method, vessels can be effectively and accurately segmented from MRA images.
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Jian Chen and Amir A. Amini "Quantifying vascular structures in MRA images using hybrid PDE and geometric deformable models", Proc. SPIE 5369, Medical Imaging 2004: Physiology, Function, and Structure from Medical Images, (30 April 2004);

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