NDLI: A low-dimensional step pattern analysis algorithm with application to multimodal retinal image registration

Content Provider	IEEE Xplore Digital Library
Author	Lee, J.A. Jun Cheng Beng Hai Lee Ee Ping Ong Guozhen Xu Wong, D.W.K. Jiang Liu Laude, A. Tock Han Lim
Copyright Year	2015
Description	Author affiliation: Nat. Healthcare Group, Tan Tock Seng Hosp., Singapore, Singapore (Laude, A.; Tock Han Lim) \|\| Inst. for Infocomm Res., Agency for Sci., Technol. & Res., Singapore, Singapore (Lee, J.A.; Jun Cheng; Beng Hai Lee; Ee Ping Ong; Guozhen Xu; Wong, D.W.K.; Jiang Liu)
Abstract	Existing feature descriptor-based methods on retinal image registration are mainly based on scale-invariant feature transform (SIFT) or partial intensity invariant feature descriptor (PIIFD). While these descriptors are often being exploited, they do not work very well upon unhealthy multimodal images with severe diseases. Additionally, the descriptors demand high dimensionality to adequately represent the features of interest. The higher the dimensionality, the greater the consumption of resources (e.g. memory space). To this end, this paper introduces a novel registration algorithm coined low-dimensional step pattern analysis (LoSPA), tailored to achieve low dimensionality while providing sufficient distinctiveness to effectively align unhealthy multimodal image pairs. The algorithm locates hypotheses of robust corner features based on connecting edges from the edge maps, mainly formed by vascular junctions. This method is insensitive to intensity changes, and produces uniformly distributed features and high repeatability across the image domain. The algorithm continues with describing the corner features in a rotation invariant manner using step patterns. These customized step patterns are robust to non-linear intensity changes, which are well-suited for multimodal retinal image registration. Apart from its low dimensionality, the LoSPA algorithm achieves about two-fold higher success rate in multimodal registration on the dataset of severe retinal diseases when compared to the top score among state-of-the-art algorithms.
Starting Page	1046
Ending Page	1053
File Size	1145805
Page Count	8
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7298707
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Retina Feature extraction Image edge detection Robustness Image registration Diseases Bifurcation
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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