by E Pellegrini, G Robertson, E Trucco, TJ MacGillivray, C Lupascu, van Hemert, J, MC Williams, DE Newby, van Beek, EJR and G Houston
Abstract:
Features of the retinal vasculature, such as vessel widths, are considered biomarkers for systemic disease. The aim of this work is to present a supervised approach to vessel segmentation in ultra-wide field of view scanning laser ophthalmoscope (UWFoV SLO) images and to evaluate its performance in terms of segmentation and vessel width estimation accuracy. The results of the proposed method are compared with ground truth measurements from human observers and with existing state-of-the-art techniques developed for fundus camera images that we optimized for UWFoV SLO images. Our algorithm is based on multi-scale matched filters, a neural network classifier and hysteresis thresholding. After spline-based refinement of the detected vessel contours, the vessel widths are estimated from the binary maps. Such analysis is performed on SLO images for the first time. The proposed method achieves the best results, both in vessel segmentation and in width estimation, in comparison to other automatic techniques.
Reference:
Blood vessel segmentation and width estimation in ultra-wide field scanning laser ophthalmoscopy (E Pellegrini, G Robertson, E Trucco, TJ MacGillivray, C Lupascu, van Hemert, J, MC Williams, DE Newby, van Beek, EJR and G Houston), In Biomed. Opt. Express, OSA, volume 5, 2014.
Bibtex Entry:
@article{Vampire14,
_author = {E Pellegrini and G Robertson and E Trucco and TJ MacGillivray and C Lupascu and van Hemert, J and MC Williams and DE Newby and van Beek, EJR and G Houston},
abstract = {Features of the retinal vasculature, such as vessel widths, are considered biomarkers for systemic disease. The aim of this work is to present a supervised approach to vessel segmentation in ultra-wide field of view scanning laser ophthalmoscope (UWFoV SLO) images and to evaluate its performance in terms of segmentation and vessel width estimation accuracy. The results of the proposed method are compared with ground truth measurements from human observers and with existing state-of-the-art techniques developed for fundus camera images that we optimized for UWFoV SLO images. Our algorithm is based on multi-scale matched filters, a neural network classifier and hysteresis thresholding. After spline-based refinement of the detected vessel contours, the vessel widths are estimated from the binary maps. Such analysis is performed on SLO images for the first time. The proposed method achieves the best results, both in vessel segmentation and in width estimation, in comparison to other automatic techniques.},
author = {E Pellegrini and G Robertson and E Trucco and TJ MacGillivray and C Lupascu and van Hemert, J and MC Williams and DE Newby and van Beek, EJR and G Houston},
date-added = {2014-11-25 18:51:17 +0000},
date-modified = {2014-11-25 18:51:17 +0000},
doi = {10.1364/BOE5.004329},
journal = {Biomed. Opt. Express},
keywords = {retinal imaging; medical},
number = {12},
pages = {4329--37},
publisher = {OSA},
title = {Blood vessel segmentation and width estimation in ultra-wide field scanning laser ophthalmoscopy},
url = {http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-5-12-4329},
volume = {5},
year = {2014},
bdsk-url-1 = {http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-5-12-4329},
bdsk-url-2 = {http://dx.doi.org/10.1364/BOE5.004329}}