脑MR图像分割和偏移场矫正的耦合水平集模型

脑核磁共振(MR)图像因需要偏移场矫正,传统分割方法很难获得准确的分割结果。针对这一问题,首先构造一组基函数拟合偏移场以保证偏移场的光滑特性,再将其融入到高斯概率密度函数中,结合统计分类准则建立脑MR图像的分割和偏移场矫正的能量方程,最后将该能量方程引入到三相位水平集的变分框架中得到脑MR图像的分割和偏移场矫正的耦合模型。实验表明该方法在得到准确的分割结果同时还可以得到较好的恢复结果。

Coupling level set model for brain MR image segmentation and bias field correction

Due to the correction of the bias field,it is hard to obtain the accurate segmentation results of magnetic resonance(MR) images using traditional methods.In this paper,a set of basis functions is constructed firstly to fit the smoothness bias field;then the information of the bias field is introduced to the Gaussian density function,and according to the statistics classification rule,we define the energy function for the brain MR image segmentation and bias field correction.At last,this energy function is incorporated into a three-phase level set framework to propose our model.Compared with other approaches,our experiments demonstrate that our method not only can obtain accurate segmentation results but also can restore images better.