基于改进的C-V模型虚拟人脑图像分割模型

C-V模型是一种较为经典的分割模型，但传统的C-V模型仅能够将图像分割成单一的目标部分与背景部分；用于彩色图像分割往往基于目标的强度信息；在曲线演化过程中需要重新初始化水平集函数保持符号距离函数。针对这些问题，使用PCA理论将颜色空间投影到新的空间中，可以扩大两者的颜色距离；使用局部信息可校正颜色强度不均匀；将距离约束项引入到模型中，使模型能够无需重新初始化，提高了演化速度。实验结果表明改进的算法能较精确地得到分割结果。

Chinese visual human images segmentation based on improved C-V model

C-V model is one of the best segmentation methods,but the classical C-V models only segment the image into object and background;only use the intensity information when segmenting color images;must re-initial the distance function during evolving the curves.In Chinese Visible Human(CVH) images,there are many fake grey matters and with the effects of these fake matters the C-V model can hardly separate grey matters with fake grey matters.To deal with the problem the PCA model is presented to large the difference of grey matters and fake grey matters.With the effects of tissues themselves,there are many in-homogenous phenomenons in the CVH images;the local information is added to model to reduce these effects.Using the distance resistance energy,the model can evolve curves without re-initialization.The Chinese visual human brain images segmentation experimental results show that the method of this paper can get right results in an accuracy way.