Speed Improvement of B-Snake Algorithm Using Dynamic Programming Optimization

摘要

This paper presents a novel approach to contour approximation carried out by means of the B-snake algorithm and the dynamic programming (DP) optimization technique. Using the proposed strategy for contour point search procedure, computing complexity is reduced to $O({Ntimes M^{2}}),$ whereas the standard DP method has an $O({Ntimes M^{4}})$ complexity, with $N$ being the number of contour sample points and $M$ being the number of candidates in the search space. The storage requirement was also decreased from $Ntimes M^{3}$ to $Ntimes M$ memory elements. Some experiments on noise corrupted synthetic image, magnetic resonance, and computer tomography medical images have shown that the proposed approach results are equivalent to those obtained by the standard DP algorithm.