title: Compounding local invariant features and global deformable geometry for medical image registration.
abstract: Using deformable models to register medical images can result in problems of initialization of deformable models and robustness and accuracy of matching of inter-subject anatomical variability. To tackle these problems, a novel model is proposed in this paper by compounding local invariant features and global deformable geometry. This model has four steps. First, a set of highly-repeatable and highly-robust local invariant features, called Key Features Model (KFM), are extracted by an effective matching strategy. Second, local features can be matched more accurately through the KFM for the purpose of initializing a global deformable model. Third, the positional relationship between the KFM and the global deformable model can be used to precisely pinpoint all landmarks after initialization. And fourth, the final pose of the global deformable model is determined by an iterative process with a lower time cost. Through the practical experiments, the paper finds three important conclusions. First, it proves that the KFM can detect the matching feature points well. Second, the precision of landmark locations adjusted by the modeled relationship between KFM and global deformable model is greatly improved. Third, regarding the fitting accuracy and efficiency, by observation from the practical experiments, it is found that the proposed method can improve 6~8% of the fitting accuracy and reduce around 50% of the computational time compared with state-of-the-art methods.
authors.affiliation: College of Computer Science, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Zhang
authors.affiliation: College of Computer Science, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Chen
authors.affiliation: College of Computer Science, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Wang
authors.affiliation: Department of Radiology, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Zhongzhao
authors.initials: Z
authors.lastname: Teng
authors.affiliation: Division of Cardiovascular Medicine, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Adam J
authors.initials: AJ
authors.lastname: Brown
authors.affiliation: Department of Radiology, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Jonathan H
authors.initials: JH
authors.lastname: Gillard
authors.affiliation: College of Computer Science, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
authors.forename: Qiu
authors.initials: Q
authors.lastname: Guan
authors.affiliation: College of Computer Science, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
authors.forename: Shengyong
authors.initials: S
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D005260:Female; D006321:Heart / Q000033:anatomy & histology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical