Gradient vector flow based on anisotropic diffusion

Authors:
Xiaosheng Yu;Chengdong Wu;Dongyue Chen;Ting Zhou;Tong Jia
Affiliations:
College of Information Science & Engineering, Northeastern University, Shenyang, China;College of Information Science & Engineering, Northeastern University, Shenyang, China;College of Information Science & Engineering, Northeastern University, Shenyang, China;College of Information Science & Engineering, Northeastern University, Shenyang, China;College of Information Science & Engineering, Northeastern University, Shenyang, China
Venue:
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Year:
2012

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Abstract

A novel external force field for active contours, called gradient vector flow based on anisotropic diffusion (ADGVF), is proposed in this paper. The generation of ADGVF contains an anisotropic diffusion process that the diffusion in the tangent and normal directions to the isophote lines has different diffusion speeds which are locally adjusted according the local structures of the image. The proposed method can address the problem associated with poor convergence of gradient vector flow in the normal direction (NGVF) to the long, thin boundary indentations and the openings of the boundaries. It can improve active contour convergence to these positions. In its numerical implementation, an efficient numerical schema is used to ensure sufficient numerical accuracy. Experimental results demonstrate that ADGVF has better performance in terms of accuracy, efficiency and robustness that that of NGVF.