耦合配准与分割的水平集演化模型

A Variational Model for Integrating Registration and Segmentation via Level Set Evolution

提出一种配准与分割耦合模型.配准项采用基于抽象匹配流的非参数配准模型,解决基于B样条的参数化配准方法与非参数活动轮廓模型在定义形式和求解方法上不一致的问题.分割项采用基于边缘的活动轮廓模型实现对感兴趣区域的分割,对分割模型的改进解决原有模型对初始化敏感的问题.整个模型直接定义在水平集函数上,定义直观,数值求解简单.对单模态及多模态大脑图像的实验,验证该模型的有效性.

A variational model for integrating registration and segmentation is proposed. A non-parametric registration method based on the abstract matching flow model is adopted as the registration term to go along with the non-parametric segmentation term, handling the problem of inconsistence on definition format and solving plan between parametric registration based on B spline and non-parametric active contour model. An edge-based active contour model is applied to segment the region of interest, and the improved model by adding region statistic information deals with the problem of sensitivity to the initialization. The integrated model is directly defined by the level set function and has the merits of intuitionstic definition and simple numerical solution. The validity of the model is verified via the experiments on single modal and multimodal brain images.