Landmark Matching Based Automatic Retinal Image Registration with Linear Programming and Self-similarities

• Published in: Information Processing in Medical Imaging Vol. 22; pp. 674 - 685
• Main Authors: Zheng, Yuanjie, Hunter, Allan A., Wu, Jue, Wang, Hongzhi, Gao, Jianbin, Maguire, Maureen G., Gee, James C.
• Format: Book Chapter Journal Article
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2011
• Series: Lecture Notes in Computer Science
• Subjects: Algorithms; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Iterative Close Point; Joint Estimation; Pattern Recognition, Automated - methods; Programming, Linear; Registration Algorithm; Reproducibility of Results; Retina - anatomy & histology; Retinal Image; Retinoscopy - methods; Sensitivity and Specificity; Subtraction Technique; Transformation Model
• ISBN: 3642220916; 9783642220913
• ISSN: 0302-9743; 1011-2499; 1611-3349
• DOI: 10.1007/978-3-642-22092-0_55

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.