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Table 5 Results comparison with existing methods on complete DRIVE database

From: A framework for retinal vasculature segmentation based on matched filters

No

Type

Methods

Year

Sen

Spe

ACC

AUC

1

 

2nd human observer

0.7796

0.9717

0.9470

2

Unsupervised methodology

Zana et al. [27]

2001

0.6971

0.9377

0.8984

3

Jiang et al. [18]

2003

0.9212

0.9114

4

Mendonca et al. [29]

2006

0.7344

0.9764

0.9452

5

Al-Diri et al. [13]

2009

0.7282

0.9551

6

Lam et al. [12]

2010

0.9472

0.9614

7

Miri et al. [26]

2011

0.7352

0.9795

0.9458

8

Fraz et al. [28]

2011

0.7152

0.9759

0.9430

9

You et al. [39]

2011

0.7410

0.9751

0.9434

10

Zhao et al. [32]

2014

0.7354

0.9789

0.9477

11

Proposed method

2015

0.7489

0.9818

0.9529

0.9664

12

Supervised methodology

Niemeijer et al. [34]

2004

0.9416

0.9294

13

Soares et al. [35]

2006

0.7332

0.9782

0.9461

0.9614

14

Staal et al. [33]

2004

0.9441

0.9520

15

Ricci et al. [11]

2007

0.9595

0.9558

16

Lupascu et al. [36]

2010

0.7200

0.9597

0.9561

17

Marin et al. [37]

2011

0.7067

0.9801

0.9452

0.9588

18

Fraz et al. [41]

2012

0.7406

0.9807

0.9480

0.9747

19

Vega et al. [38]

2015

0.7444

0.9600

0.9412

  1. –, means the value is not answered in the reference paper
  2. Italic numbers denote the best cases among unsupervised methods and the comparatively better results among supervised ones in terms each evaluation criterion