Landmark matching based automatic retinal image registration with linear programming and self-similarities

Authors:
Yuanjie Zheng;Allan A. Hunter, III;Jue Wu;Hongzhi Wang;Jianbin Gao;Maureen G. Maguire;James C. Gee
Affiliations:
PICSL, Department of Radiology, University of Pennsylvania, Philadelphia, PA;Department of Ophthalmology, University of Pennsylvania, Philadelphia, PA;PICSL, Department of Radiology, University of Pennsylvania, Philadelphia, PA;PICSL, Department of Radiology, University of Pennsylvania, Philadelphia, PA;School of Computer Science & Engineering, University of Electronic Science and Technology of China;Department of Ophthalmology, University of Pennsylvania, Philadelphia, PA;PICSL, Department of Radiology, University of Pennsylvania, Philadelphia, PA
Venue:
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Year:
2011

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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.