Snake Model Based On Optimization Of Parameter And The Opplications In MEI Image Segmetation

Image segmentation is a regional breakdown of the image to extract interesting features of the target with the technology and processes. Medical image processing is a branch of the computer field is one of the digital images processing in biomedical engineering in important applications. In recent years, image segmentation based on active contour model, with its flexible structure, a variety of forms, and superior performance is attracted by domestic and foreign scholar's attention. In the model many mathematics, statistics, physics and other theories are absorbed. Parametric active contour model has the unique characteristics of highly dynamic, making it ideal for medical image segmentation. After reading large amounts of data, the model is researched in medical image segmentation related technical problems in paper.Firstly, after understanding the overview of medical image segmentation method of medical image research status, the method of solving physical meaning, the algorithm process and the characteristics of the traditional model is introduced, from Kass et al in 1987 proposed the Snake model. MRI principle of image description and image features are shortly introduced.Secondly, for improved Snake model, analysis and comparison of force improvement and optimization of parameters; about the Balloon Snake, GVF Snake and the Greedy Snake,derivate the formula, contrast the experiments, summarize the suitable situation of the models.Finally, the analysis of parametric active contour model of the impact of parameters on the segmentation results, a full understanding of the model, based on the parameters and the relationship between pixel coordinates map is proposed. The approximate range of parameters is determined by the intersection of the boundary with the goal and diagram, find the accurate range of parameters using experiments.Based on the above theory, do a lot of the simulation of the brain MRI and abdominal MRI, test and verify the validity range of parameters that is determined.