title: Robust adaptive principal component analysis based on intergraph matrix for medical image registration.
abstract: This paper proposes a novel robust adaptive principal component analysis (RAPCA) method based on intergraph matrix for image registration in order to improve robustness and real-time performance. The contributions can be divided into three parts. Firstly, a novel RAPCA method is developed to capture the common structure patterns based on intergraph matrix of the objects. Secondly, the robust similarity measure is proposed based on adaptive principal component. Finally, the robust registration algorithm is derived based on the RAPCA. The experimental results show that the proposed method is very effective in capturing the common structure patterns for image registration on real-world images.
authors.affiliation: Key Laboratory of Nondestructive Testing, Ministry of Education, School of Mathematics and Information Science, Nanchang Hangkong University, Nanchang 330063, China ; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Chengcai
authors.identifier: 0000-0003-4535-5430
authors.initials: C
authors.lastname: Leng
authors.affiliation: Information Center, Jiangxi School of Electronics and Information Engineering, Nanchang 330096, China.
authors.forename: Jinjun
authors.initials: J
authors.lastname: Xiao
authors.affiliation: School of Material Science and Engineering, Nanchang Hangkong University, Nanchang 330063, China.
authors.forename: Min
authors.initials: M
authors.lastname: Li
authors.affiliation: Key Laboratory of Nondestructive Testing, Ministry of Education, School of Mathematics and Information Science, Nanchang Hangkong University, Nanchang 330063, China.
authors.forename: Haipeng
authors.initials: H
authors.lastname: Zhang
mesh_terms: D001921:Brain / Q000033:anatomy & histology*; D003952:Diagnostic Imaging / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D025341:Principal Component Analysis