Table 1.

Comparison of Vessel Segmentation Algorithms

Method	Authors	Year	Data Set	Sensitivity	Specificity	Overall Accuracy
Ensemble classifiers-based methods	Orlando et al	2014	DRIVE	0.78	0.97	N/A
	Orlando et al	2017	DRIVE	0.79	0.97	N/A
	Lupascu et al	2010	DRIVE	0.67	0.99	0.96
	Fraz et al	2012	DRIVE	0.74	0.98	0.95
Statistical learning-based methods	Staal et al	2004	DRIVE	N/A	N/A	0.94
	Soares et al	2006	DRIVE	N/A	N/A	0.95
Neural network	Marin et al	2011	DRIVE	0.71	0.98	0.94
	Vega et al	2014	DRIVE	0.74	0.96	0.94
	Wang et al	2015	DRIVE	0.82	0.97	0.98
	Li et al	2016	DRIVE	0.76	0.98	0.95
	Mo et al	2017	DRIVE	0.78	0.98	0.95
	Xu et al	2018	DRIVE	0.94	0.96	0.95
	Yan et al	2018	DRIVE	0.76	0.98	0.95
	Proposed method	2021	DRIVE	0.78	0.99	0.97

DRIVE = Digital Retinal Images for Vessel Extraction; N/A = not available.