title: Landmark matching based automatic retinal image registration with linear programming and self-similarities.
abstract: In this paper, we address the problem of landmark matching based retinal image registration. Two major contributions render our registration algorithm distinguished from many previous methods. One is a novel landmark-matching formulation which enables not only a joint estimation of the correspondences and transformation model but also the optimization with linear programming. The other contribution lies in the introduction of a reinforced self-similarities descriptor in characterizing the local appearance of landmarks. Theoretical analysis and a series of preliminary experimental results show both the effectiveness of our optimization scheme and the high differentiating ability of our features.
authors.affiliation: PICSL, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Yuanjie
authors.initials: Y
authors.lastname: Zheng
authors.forename: Allan A
authors.initials: AA
authors.lastname: Hunter
authors.forename: Jue
authors.initials: J
authors.lastname: Wu
authors.forename: Hongzhi
authors.initials: H
authors.lastname: Wang
authors.forename: Jianbin
authors.initials: J
authors.lastname: Gao
authors.forename: Maureen G
authors.initials: MG
authors.lastname: Maguire
authors.forename: James C
authors.initials: JC
authors.lastname: Gee
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011382:Programming, Linear; D015203:Reproducibility of Results; D012160:Retina / Q000033:anatomy & histology*; D042262:Retinoscopy / Q000379:methods*; D012680:Sensitivity and Specificity; D013382:Subtraction Technique