[与机器学习相结合的磁共振脑图像非线性配准]
[Non-linear registration of MR brain images integrated with machine learning].
作者信息
Wu Guo-rong, Qi Fei-hu
机构信息
Dept. of Computer Science & Engineering, Shanghai Jiao Tong University, Shanghai, 200030.
出版信息
Zhongguo Yi Liao Qi Xie Za Zhi. 2006 Jul;30(4):268-70.
PMID:17039935
Abstract
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.
摘要
本文提出了一种机器学习方法，用于为每个脑区选择用于可变形脑配准的最佳几何特征。通过将那些学习到的最佳属性向量纳入HAMMER配准算法框架，在估计模拟变形场时精度提高了约10%。同时，在真实的脑部磁共振图像上，我们发现在皮质区域配准有了很大改进。
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