by Zutis, K, Trucco, E, Hubschman, JP, Reed, D, Shah, S and van Hemert, J
Abstract:
Retinal capillary abnormalities include small, leaky, severely tortuous blood vessels that are associated with a variety of retinal pathologies. We present a prototype image-processing system for detecting abnormal retinal capillary regions in ultra-widefield-of-view (UWFOV) fluorescein angiography exams of the human retina. The algorithm takes as input an UWFOV FA frame and returns the candidate regions identified. An SVM classifier is trained on regions traced by expert ophthalmologists. Tests with a variety of feature sets indicate that edge features and allied properties differentiate best between normal and abnormal retinal capillary regions. Experiments with an initial set of images from patients showing branch retinal vein occlusion (BRVO) indicate promising area under the ROC curve of 0.950 and a weighted Cohen's Kappa value of 0.822.
Reference:
Towards automatic detection of abnormal retinal capillaries in ultra-widefield-of-view retinal angiographic exams (Zutis, K, Trucco, E, Hubschman, JP, Reed, D, Shah, S and van Hemert, J), In Conf Proc IEEE Eng Med Biol Soc, 2013.
Bibtex Entry:
@article{ZutisTrucco2013fk,
abstract = {Retinal capillary abnormalities include small, leaky, severely tortuous blood vessels that are associated with a variety of retinal pathologies. We present a prototype image-processing system for detecting abnormal retinal capillary regions in ultra-widefield-of-view (UWFOV) fluorescein angiography exams of the human retina. The algorithm takes as input an UWFOV FA frame and returns the candidate regions identified. An SVM classifier is trained on regions traced by expert ophthalmologists. Tests with a variety of feature sets indicate that edge features and allied properties differentiate best between normal and abnormal retinal capillary regions. Experiments with an initial set of images from patients showing branch retinal vein occlusion (BRVO) indicate promising area under the ROC curve of 0.950 and a weighted Cohen's Kappa value of 0.822.},
annote = {Retinal capillary abnormalities include small, leaky, severely tortuous blood vessels that are associated with a variety of retinal pathologies. We present a prototype image-processing system for detecting abnormal retinal capillary regions in ultra-widefield-of-view (UWFOV) fluorescein angiography exams of the human retina. The algorithm takes as input an UWFOV FA frame and returns the candidate regions identified. An SVM classifier is trained on regions traced by expert ophthalmologists. Tests with a variety of feature sets indicate that edge features and allied properties differentiate best between normal and abnormal retinal capillary regions. Experiments with an initial set of images from patients showing branch retinal vein occlusion (BRVO) indicate promising area under the ROC curve of 0.950 and a weighted Cohen's Kappa value of 0.822. },
author = {Zutis, K and Trucco, E and Hubschman, JP and Reed, D and Shah, S and van Hemert, J},
date-added = {2014-04-26 18:21:47 +0000},
date-modified = {2014-04-26 18:29:47 +0000},
doi = {10.1109/EMBC2013.6611261},
journal = {Conf Proc IEEE Eng Med Biol Soc},
keywords = {retinal imaging; medical},
pages = {7372--5},
title = {Towards automatic detection of abnormal retinal capillaries in ultra-widefield-of-view retinal angiographic exams},
year = {2013},
bdsk-url-1 = {http://dx.doi.org/10.1109/EMBC2013.6611261}}