@conference{omia 27656, author = {Roberto Annunziata, Ahmad Kheirkhah, Shruti Aggarwal, Bernardo M. Cavalcanti, Pedram Hamrah, Emanuele Trucco}, title = {Tortuosity classification of corneal nerves images using a multiple-scale-multiple-window approach}, volume = {1}, year = {2014}, url = {https://pubs.lib.uiowa.edu/omia/article/id/27656/}, issue = {2014}, doi = {10.17077/omia.1016}, abstract = {<p>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.</p>}, month = {9}, pages = {113-120}, publisher={University of Iowa}, journal = {Proceedings of the Ophthalmic Medical Image Analysis International Workshop} }