Retina Image Vessel Segmentation Using a Hybrid CGLI Level Set Method

Table 2

The performance of our CGLI method on the DRIVE data and STARE data compared with other methods. The result of our method is in italic.

	Method	DRIVE			STARE
		sn	sp	acc	sn	sp	acc
Unsupervised method	CGLI	0.7358	0.9680	0.9390	0.7449	0.9690	0.9409
	Azzopardi et al. (2015) [1]	0.7655	0.9704	0.9442	0.7716	0.9701	0.9497
	Vlachos and Dermatas (2010) [8]	0.7468	0.9551	0.9285	0.7455	0.9544	0.9270
	Martí et al. (2007) [9]	0.6634	0.9682	0.9352	0.6701	0.9599	0.9371
Supervised method	Marín et al. (2011) [10]	NA	NA	0.9452	NA	NA	0.9344
	Fraz et al. (2012) [11]	0.7152	0.9759	0.9430	0.7311	0.9680	0.9442