Information Gain-based Initialization for Stable Topological Derivative Segmentation
ongsang Cho, Nam In Park, Boeun Kim, Young Han Lee, In Hye Yoon · TECHART Journal of Arts and Imaging Science · 2018
This paper describes topological derivatives for image segmentation and restoration. Segmentation performance based on topological derivatives and level set methods heavily depends on initialization information. To achieve stable and accurate segmentation, an efficient initialization scheme is proposed by modeling an image statistically and by analyzing the modeling result based on information theory and classical test theory. Specifically, an image is modeled using a Gaussian mixture model (GMM) for observed data and hidden data; the model information is derived through the maximization of the likelihood distribution and evaluated using the information theory and classical test theory to obtain the weight factors of GMM and class initials. The experimental results demonstrate that the segmentation performance of the proposed method is more stable and accurate than that of the existing algorithms in terms of visual quality and speed.