Medical Object Extraction by Gaussian Mixture Model and Region Competition Active Contour Model
基于高斯混合分布和区域竞争主动轮廓的医学目标分割

This paper proposed a regional active contour model with an embedded classifier, based on a Gaussian mixlure model fitted to the intensity distribution of the medical image. The difference between the maximum probability of the intensities belonging to the classes or subclasses of the object and those of the background is made as an energy term in the active contour modcl,and minimization of the whole energy function leads to a novel iterative equation. An additional speed controlling term slows down the evolution of the active contour when it approaches an edge, making it quickly convergent to the ideal object. The developed model has been applied to liver segmentation. Some comparisons are made between the geodesic active contour,C-V(active contour without edges),manual outline and our model. As the experiments show that our model is accurate,flexible and suited to extract objects surrounded by a complicated back-ground.