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11 March 2008 Robust segmentation of tubular structures in medical images
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Segmentation of blood vessels is a challenging problem due to poor contrast, noise, and specifics of vessels' branching and bending geometry. This paper describes a robust semi-automatic approach to extract the surface between two or more user-supplied end points for tubular- or vessel-like structures. We first use a minimal path technique to extract the shortest path between the user-supplied points. This path is the global minimizer of an active contour model's energy along all possible paths joining the end-points. Subsequently, the surface of interest is extracted using an edge-based level set segmentation approach. To prevent leakage into adjacent tissues, the algorithm uses a diameter constraint that does not allow the moving front to grow wider than the predefined diameter. Points constituting the extracted path(s) are automatically used as initialization seeds for the evolving level set function. To cope with any further leaks that may occur in the case of large variations of the vessel width between the user-supplied end-points, a freezing mechanism is designed to prevent the moving front to leak into undesired areas. The regions to be frozen are determined from few clicks by the user. The potential of the proposed approach is demonstrated on several synthetic and real images.
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Rachid Fahmi, Anna Jerebko, Matthias Wolf, and Aly A. Farag "Robust segmentation of tubular structures in medical images", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 691443 (11 March 2008);

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