A new image segmentation model with local statistical characters based on variance minimization

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 segmentation method based on CV model 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 and introduced local statistics in the new energy formulation. 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. The major contributions of this paper conclude two aspects. One is the new energy function based on variance minimization and another is the introduction of the local weighted averaging. In this paper, we get the local weighted averaging by the pieces smooth approximation through Gaussian convolution. Experimental results demonstrate that the proposed approach is effective in image segmentation, especially for the image with in-homogeneity intensity.