Geometric active curve for selective entropy optimization

Abstract

Recently, with the development of high dimensional large-scale medical imaging devices, the need of fast, robust and accurate segmentation methods is increasing. In this paper, we propose a new level set method (LSM) for image segmentation. The basic idea is to design a selective entropy-based energy functional which is effective and robust against noise, from which we will derive the level set equations and a new selective entropy external forces for the lattice Boltzmann D2Q5 partial differential equation (PDE) solver. The method is accurate and highly parallelizable. The local nature of the lattice Boltzmann method (LBM) allows it to be suitable for fast segmentation methods implemented using some parallel devices such as the graphics processing unit. The proposed algorithm is effective, robust against noise and highly parallelizable. Furthermore, the method can easily be extended to perform an effective image filtering based on Gaussian fuzzy selection. Experimental results on medical images demonstrate subjectively and objectively the performance of the proposed method.