高级检索

基于改进光流场模型的大脑图像配准

An Improved Optical Flow Model for Brain Image Registration

  • 摘要: 将光流场模型引入大脑图像配准,针对Horn模型会造成图像严重模糊的问题,在微分光流场模型的一般框架下,构造具有边缘保持和一致性增强能力的流驱动各向异性扩散方程作为正则项,以增强配准过程的特征保持能力;采用非二次惩罚函数作为数据项,以增强模型的鲁棒性.最后利用文中模型对大脑图像进行配准实验,得到了较为准确的结果.

     

    Abstract: An improved optical flow model within the differential framework is employed in brain image registration.Aiming at eliminating the severe image blurring caused by the Horn model,the anisotropic flow-driven diffusion,which is able to preserve edges and enhance coherence,is used as the regularization term to keep the image feature during the registration process.The data term employs the nonquadratic penalization function to improve the model's robustness.Our improved model is applied to register brain images,and more accurate and robust results are obtained.

     

/

返回文章
返回