Decomposition and Active Contour Method for Medical Noise Image Segmentation

Segmentation on medical noise images is a very challenging research task.This paper proposes an image segmentation model based on decomposition and active contour for simultaneous image de-noising and segmentation.The model is a variational functional integrating the image decomposition model in G space and the active contour model combining the boundary and regional information.The model solution can be decomposed into solving two functional extremes-image decomposition part and segmentation part to avoid the difficulty of searching for a direction solution.the image decomposition part solution is a functional extreme in G space and can be solved by threshold shrinkage in the second generation curvelet.The segmentation part solution is a variational level set functional extreme and its corresponding Euler equations is the nonlinear partial differential equation which can be solved by gradient descent flow.Experimental results show that the suggested model in this paper not only de-noises effectively but also has better segmentation performance than the Chan-Vese model,snake model,variational level-set model and the ASM under the same experimental conditions.Our method improves image quality and separates target part well.