Open AccessMulti-modal automatic montaging of adaptive optics retinal images
Author(s) -
Min Chen,
Robert F. Cooper,
Grace Han,
James C. Gee,
David H. Brainard,
Jessica Ijams Wolfing Morgan
Publication year - 2016
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.7.004899
Subject(s) - computer science , artificial intelligence , classification of discontinuities , computer vision , discontinuity (linguistics) , adaptive optics , pattern recognition (psychology) , image processing , algorithm , image (mathematics) , optics , mathematics , mathematical analysis , physics
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.