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12 March 2010 Affinity-based constraint optimization for nearly-automatic vessel segmentation
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We present an affinity-based optimization method for nearly-automatic vessels segmentation in CTA scans. The desired segmentation is modeled as a function that minimizes a quadratic affinity-based functional. The functional incorporates intensity and geometrical vessel shape information and a smoothing constraint. Given a few user-defined seeds, the minimum of the functional is obtained by solving a single set of linear equations. The binary segmentation is then obtained by applying a user-selected threshold. The advantages of our method are that it requires fewer initialization seeds, is robust, and yields better results than existing graph-based interactive segmentation methods. Experimental results on 20 vessel segments including the carotid arteries bifurcation and noisy parts of the carotid yield a mean symmetric surface error of 0.54mm (std=0.28).
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
O. Cooper, M. Freiman, L. Joskowicz, and D. Lischinski "Affinity-based constraint optimization for nearly-automatic vessel segmentation", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76230O (12 March 2010);

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