Non-linear registration of MR brain images integrated with machine learning.
- Author:
Guo-rong WU
1
;
Fei-hu QI
Author Information
1. Dept. of Computer Science & Engineering, Shanghai Jiao Tong University, Shanghai, 200030.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Artificial Intelligence;
Brain;
anatomy & histology;
Computer Simulation;
Humans;
Image Enhancement;
methods;
Image Interpretation, Computer-Assisted;
methods;
Magnetic Resonance Imaging;
methods;
Pattern Recognition, Automated;
methods;
Reproducibility of Results
- From:Chinese Journal of Medical Instrumentation
2006;30(4):268-270
- CountryChina
- Language:Chinese
-
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.
