Research Article Adaptive Thresholding Technique for Retinal Vessel Segmentation Based on GLCM-Energy Information Temitope Mapayi, 1 Serestina Viriri, 1 and Jules-Raymond Tapamo 2 1 School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Durban 4000, South Africa 2 School of Engineering, University of KwaZulu-Natal, Durban 4041, South Africa Correspondence should be addressed to Jules-Raymond Tapamo; tapamoj@ukzn.ac.za Received 9 August 2014; Revised 24 November 2014; Accepted 24 November 2014 Academic Editor: Lev Klebanov Copyright © 2015 Temitope Mapayi et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Although retinal vessel segmentation has been extensively researched, a robust and time eicient segmentation method is highly needed. his paper presents a local adaptive thresholding technique based on gray level cooccurrence matrix- (GLCM- ) energy information for retinal vessel segmentation. Diferent thresholds were computed using GLCM-energy information. An experimental evaluation on DRIVE database using the grayscale intensity and Green Channel of the retinal image demonstrates the high performance of the proposed local adaptive thresholding technique. he maximum average accuracy rates of 0.9511 and 0.9510 with maximum average sensitivity rates of 0.7650 and 0.7641 were achieved on DRIVE and STARE databases, respectively. When compared to the widely previously used techniques on the databases, the proposed adaptive thresholding technique is time eicient with a higher average sensitivity and average accuracy rates in the same range of very good speciicity. 1. Introduction Retinal fundus imaging in ophthalmology is of great use in medical diagnosis and progression monitoring of several diseases like hypertension, diabetes, stroke, and cardiovas- cular disease [1]. Automatic vessel segmentation has a great potential to assist in the reduction of the time required by physicians or skilled technicians for manual labeling of retinal vessels [2]. Several retinal vessel segmentation techniques have been proposed and evaluated in literatures. Chaudhuri et al. [3] implemented a two-dimensional matched ilter using a Gaussian shaped curve. However, the technique proposed in [3] achieved very low average accuracy due to low detection of retinal vessels. Hoover [4] segmented retinal vessels by applying a threshold probing technique combining local ves- sel attributes with region-based attributes on matched ilter response (MFR) image. When compared to [3] where a basic thresholding of an MFR was used, the method proposed by [1] reduced the false positive rate by as much as 15 times. Fraz et al. [5] implemented vessel segmentation technique utilizing extracted center-lines of retinal vessels through irst-order derivative of Gaussian ilter. he authors used morphological operator with directional structuring elements to enhance the structure of blood vessels. hey further generated the shape and orientation map of the blood vessels using the bit planes of a gray-scale image. Chakraborti et al. [6] implemented an unsupervised segmentation technique that combines ves- selness ilter and matched ilter using orientation histogram for the segmentation of retinal vessels. Martinez-Perez et al. [7] used a combination of scale space analysis and region growing to segment the vasculature. he technique proposed in [7] was however unable to segment the thin vessels. Zana and Klein [8] used a general vessel segmentation method based on mathematical morphology. However, the technique proposed in [8] was unable to segment the thinner vessels. Wang et al. [9] proposed multiwavelet kernels and mul- tiscale hierarchical decomposition. Vessels were enhanced using matched iltering with multiwavelet kernels. Szpak and Tapamo [10] used gradient based approach and level set tech- nique. he proposed technique in [10] was however unable to detect the thinner vessels. Vlachos and Dermatas [11] proposed a multiscale retinal vessel segmentation method. he algorithm is based on multiscale line-tracking procedure Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2015, Article ID 597475, 11 pages http://dx.doi.org/10.1155/2015/597475