非线性立体脑图像配准中的机器学习方法

Non-Linear Registration of MR Brain Images Integrated with Machine Learning

针对基于属性向量的非线性配准算法,提出用机器学习的方法寻找脑图像中各个点上的最优几何特征向量。通过定义一个能量函数把寻找最优属性向量的过程归结为一个最优化问题。把训练得到的最优属性向量与HAMMER(一种基于属性向量的非线性配准算法)相结合,对模拟的MR脑图像进行了实验．与HAMMER相比,位移场的精度提高了10%。改进后的算法对真实的MR脑图像的配准结果,也有很大的改善。

This paper presents a machine learning method to select best geometric features for deformable brain registration for each brain location. By incorporating those learned best attribute vector into the framework of HAMMER registration algorithm, The accuracy has increased by about 10% in estimating the simulated deformation fields. At the same time on real MR brain images, we have found a great deal of improvement of registration in cortical regions.