Journal
BIOMEDICAL OPTICS EXPRESS
Volume 7, Issue 12, Pages 4899-4918Publisher
Optica Publishing Group
DOI: 10.1364/BOE.7.004899
Keywords
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Funding
- National Eye Institute, National Institute of Health (NEI, NIH) [P30 EY001583, U01 EY025477]
- Research To Prevent Blindness Stein Innovation Award
- Foundation Fighting Blindness
- F. M. Kirby Foundation
- Paul and Evanina Mackall Foundation Trust
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We present a fully automated adaptive optics (AO) retinal image montaging algorithm using classic scale invariant feature transform with random sample consensus for outlier removal. Our approach is capable of using information from multiple AO modalities (confocal, split detection, and dark field) and can accurately detect discontinuities in the montage. The algorithm output is compared to manual montaging by evaluating the similarity of the overlapping regions after montaging, and calculating the detection rate of discontinuities in the montage. Our results show that the proposed algorithm has high alignment accuracy and a discontinuity detection rate that is comparable (and often superior) to manual montaging. In addition, we analyze and show the benefits of using multiple modalities in the montaging process. We provide the algorithm presented in this paper as open-source and freely available to download. (C) 2016 Optical Society of America
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