Tortuosity classification of corneal nerves images using a multiple-scale-multiple-window approach
Classify in vivo confocal microscopy corneal images by tortuosity is complicated by the presence of variable numbers of fibres of different tortuosity level. Instead of designing a function combining manually selected features into a single coefficient, as done in the literature, we propose a supervised approach which selects automatically the most relevant combination of shape features from a pre-defined dictionary. To our best knowledge, we are the first to consider features at different spatial scales and show experimentally their relevance in tortuosity modelling. Our results, obtained with a set of 100 images and 20 fold cross-validation, suggest that multinomial logistic ordinal regression, trained on consensus ground truth from 3 experts, yields an accuracy indistinguishable, overall, from that of experts when compared against each other.
How to Cite:
Annunziata, R. & Kheirkhah, A. & Aggarwal, S. & Cavalcanti, B. M. & Hamrah, P. & Trucco, E., (2014) “Tortuosity classification of corneal nerves images using a multiple-scale-multiple-window approach”, Ophthalmic Medical Image Analysis International Workshop 1(2014), p.113-120. doi: https://doi.org/10.17077/omia.1016
Rights: Copyright © 2014, Roberto Annunziata, Ahmad Kheirkhah, Shruti Aggarwal, Bernardo M. Cavalcanti, Pedram Hamrah, and Emanuele Trucco.