A novel variance minimization segmentation model

Authors:
Bo Chen;Yan Li;Wen-Sheng Chen;Jin-Lin Cai
Affiliations:
College of Mathematics and Computational Science, Shenzhen University, Shenzhen, P.R. China;College of Mathematics and Computational Science, Shenzhen University, Shenzhen, P.R. China;College of Mathematics and Computational Science, Shenzhen University, Shenzhen, P.R. China;College of Mathematics and Computational Science, Shenzhen University, Shenzhen, P.R. China
Venue:
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Year:
2012

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Abstract

Chan-Vese (CV) model is a promising active contour model for image segmentation. However, CV model does not utilize local region information of images and thus CV model based segmentation methods cannot achieve good segmentation results for complex image with some in-homogeneity intensities. To overcome the limitation of CV model, this paper presents a new type of geometric active contour model using the strategy of variance minimization of image. The proposed model not only considers the first and second order moments of objective image statistical measurements, but also regularizes the level set function by incorporating the distance penalized energy function. Extensive experimental results demonstrate that the proposed approach is effective in image segmentation, especially for the image with in-homogeneity intensity.