A fast snake model based on non-linear diffusion for medical image segmentation

Abstract

In this paper, the traditional snake model and gradient vector flow (GVF) snake model are studied, which are believed to be quite slow due to the need to compute inverse matrix. Actually, the GVF in the latter snake model is formed by a biased linear diffusion procedure, and there would be oscillations around the edge of the object. Based on GVF generated through non-linear diffusion, we present a fast GVF (FGVF) snake model which is much faster than the traditional snake model and GVF snake model, and would cause no degradation of stability and flexibility, meanwhile, it could reduce the oscillations around the edges. The segmentation results using FGVF and error analysis on simulated images are presented. Finally, the demonstration of FGVF applied to Computed Tomography and Magnetic Resonance images are shown, the segmentation results are satisfactory visually with much less computation time in comparison with former snakes.