A Hybrid Image Segmentation Model Based on GMM and CV Model

Abstract

Traditional CV level set segmentation model is vulnerable to image noise and non-uniform gray level in the target area, which affects the segmentation accuracy. In this paper, We propose a new image segmentation model, which combines Gaussian mixture model(GMM) and CV level set image segmentation algorithm. We use the GMM foreground detection result as important prior information of CV level set image segmentation in order to integrate multi-information. We add different gray values to foreground area and background area to increase contrast. We put the new different gray values into the level set function to construct new energy terms and it effectively minimize inaccurate edge location caused by the image noise and uneven gray scale in image. In this paper, our method compares to LIF, LBF, RSF and other CV level set image segmentation algorithm, and the experiment result shows that our method is better than others and achieves faster convergence.