Fast and robust image segmentation with active contours and Student's-t mixture model

In this paper, a novel active contours method, which combines with the Student's-t mixture model via Expectaton-Maximizaton (EM) algorithm, is proposed to segment complicated two-phase images. Firstly, we rewrite the cost function and derive a novel updating of level set function based on probabilistic principles. Secondly, we put forward two novel geometric priors from the level-set-based curve evolution; and both of them have advantages, the suitable one is selected by personalized need to obtain level set function in EM framework with the aim of reducing the computational cost. Therefore, the level set function is derived from latent variables and served as a feedback to the estimation of the latent variables in next iteration. Finally, in order to enhance the robustness to the outliers, Student's-t mixture model with heavy tail has been applied in our algorithm. Experimental results obtained by employing the proposed method on many synthetic, medical and real-world images to demonstrate its robustness, accuracy and effectiveness.