Medical Images Segmentation Using a Novel Level Set Model with Laplace Kernel Function

摘要

Medical image segmentation is a complex study due to its disadvantages such as noise, low-contrast, intensity inhomogeneity, and so on. A novel level set model was proposed in this study to segment medical images accurately. The kernel function used to determine the size of neighborhood of central pixel was modified by Laplace kernel function, which is insensitive to the choice of parameters and is more suitable for segmenting medical images. Compared with several state-of-the-art models, both visual and objective experiments can demonstrate the performance and superiority of the novel level set model.