Cell Image Segmentation Based On Snake Model

The increasingly important role of medical imaging in the diagnosis and treatment of disease has opened an array of challenging problems centered on the computation of accurate geometric models of cell from medical images. A promising approach to tackle such problems is the use of active contour models. These powerful models have proven to be effective in research the segmenting, visualizing, matching cell by exploiting constraints derived from the image data together with a priori knowledge about the location, size and shape of these structures. Furthermore, active contour models support highly intuitive interaction mechanisms that allow medical scientists and practitioners to bring their expertise to bear on the image interpretation task.The focus of this dissertation is to investigate active contour models. The interior limitation of the traditional Snake model has caused the severe disadvantage, which is the disability to extract boundary concavities. It is difficult for traditional balloon force model to deal with automatic segmentation of weak edge medical image in image segmentation. GVF model is a kind of representative active contour model. It derives from generalized force balance equations, a vector diffusion equation is employed to diffuse the gradient of an edge map in regions distant from the boundary, yielding a general type of force field which can be decomposed in some status. The main works in this dissertation are as follows: 1. Solving the problem of selecting initial contour, solving the problem of automatic selecting the parameters of the model. 2. Implementing greedy algorithm for snake model. 3.Improving the snake model, to make snake model perform better convergence.