Improved Algorithm for Gradient Vector Flow Based Active Contour Model Using Global and Local Information

Year: 2013

Type: article

Abstract: Active contour models are used to extract object boundary from digital image, but there is poor convergence for the targets with deep concavities. We proposed an improved approach based on existing gradient vector flow methods. Main contributions of this paper are a new algorithm to determine the false part of active contour with higher accuracy from the global force of gradient vector flow and a new algorithm to update the external force field together with the local information of magnetostatic force. Our method has a semidynamic external force field, which is adjusted only when the false active contour exists. Thus, active contours have more chances to approximate the complex boundary, while the computational cost is limited effectively. The new algorithm is tested on irregular shapes and then on real images such as MRI and ultrasound medical data. Experimental results illustrate the efficiency of our method, and the computational complexity is also analyzed.

Source: The Scientific World JOURNAL

DOI: 10.1155/2013/479675

Authors: Jianhui Zhao, Bingyu Chen, Mingui Sun, Wenyan Jia, Zhiyong Yuan

Institutions: Wuhan University, University of Pittsburgh

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $860

Funders: National Natural Science Foundation of China, Natural Science Foundation of Hubei Province, Suzhou Municipal Science and Technology Bureau, Wuhan Municipal Science and Technology Bureau, National Key Research and Development Program of China

OpenAlex ID: W2065217440