改进的ChanVese活动轮廓模型

Improved ChanVese Active Contour Model

针对ChanVese模型计算量大和分割时间长的问题,提出了一种改进的ChanVese活动轮廓模型。该模型将水平集规则式融入ChanVese模型中,使水平集函数始终保持在符号距离函数附近,避免了重新初始化过程,减少了模型的计算量,同时改变了水平集函数的初始化函数,这更有利于新曲线的产生。实验结果表明,该改进模型具有一定的抗噪性,收敛速度快,分割时间少,且能得到全局最优的分割结果。

Because the complexity of ChanVese model is expensive and its segmentation time is long, an improved ChanVese active contour model is presented. In the model, a level set regularization term is introduced into ChanVese model, which can force level set function close to a signed distance function so that re-initialization can be avoided, and the complexity of ChanVese model is less expensive, and we also change the function which is set as the initial level set function, so that new contours emerge more easily. Finally the experimental results demonstrate that the improved ChanVese model is robust to noise, converges more quickly, and can achieve global segmentation.