GPU Accelerated Level Set Non-Homogenous Image Segmentation Solving by Lattice Boltzmann Method

摘要

A novel hybrid fitting energy based active contours model in the level set framework is proposed. The method fuses the local image fitting term and the global image fitting term to drive the contour evolution. Our model can efficiently segment the images with intensity inhomogeneity no matter where the initial curve is located in the image. In its numerical implementation, an efficient numerical scheme called Lattice Boltzmann Model (LBM) is used to break the restrictions on time step, compared with the traditional schemes, the LBM strategy can further shorten the time consumption of the evolution process, this allows the level set to quickly reach the true target location. In addition, the proposed LSM is implemented using an NVIDIA graphics processing units (GPU) to fully take advantage of the LBM local nature. The extensive and promising experimental results on synthetic and real images demonstrate subjectively and objectively the performance of the proposed method.