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Parametric kernel-driven active contours for image segmentation

[+] Author Affiliations
Qiongzhi Wu

Beijing Institute of Technology, School of Information and Electronics, Beijing 100081, China

Jiangxiong Fang

East China Institute of Technology, Department of Nuclear Engineering and Physical Geography, Nanchang 330013, China

J. Electron. Imaging. 21(4), 043015 (Nov 22, 2012). doi:10.1117/1.JEI.21.4.043015
History: Received July 3, 2012; Revised September 20, 2012; Accepted October 23, 2012
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Abstract.  We investigated a parametric kernel-driven active contour (PKAC) model, which implicitly transfers kernel mapping and piecewise constant to modeling the image data via kernel function. The proposed model consists of curve evolution functional with three terms: global kernel-driven and local kernel-driven terms, which evaluate the deviation of the mapped image data within each region from the piecewise constant model, and a regularization term expressed as the length of the evolution curves. In the local kernel-driven term, the proposed model can effectively segment images with intensity inhomogeneity by incorporating the local image information. By balancing the weight between the global kernel-driven term and the local kernel-driven term, the proposed model can segment the images with either intensity homogeneity or intensity inhomogeneity. To ensure the smoothness of the level set function and reduce the computational cost, the distance regularizing term is applied to penalize the deviation of the level set function and eliminate the requirement of re-initialization. Compared with the local image fitting model and local binary fitting model, experimental results show the advantages of the proposed method in terms of computational efficiency and accuracy.

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Citation

Qiongzhi Wu and Jiangxiong Fang
"Parametric kernel-driven active contours for image segmentation", J. Electron. Imaging. 21(4), 043015 (Nov 22, 2012). ; http://dx.doi.org/10.1117/1.JEI.21.4.043015


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