title: [Non-linear registration of MR brain images integrated with machine learning].
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.
authors.affiliation: Dept. of Computer Science & Engineering, Shanghai Jiao Tong University, Shanghai, 200030.
authors.forename: Guo-rong
authors.initials: GR
authors.lastname: Wu
authors.forename: Fei-hu
authors.initials: FH
authors.lastname: Qi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results