title: Adaptive thresholding technique for retinal vessel segmentation based on GLCM-energy information.
abstract: Although retinal vessel segmentation has been extensively researched, a robust and time efficient segmentation method is highly needed. This paper presents a local adaptive thresholding technique based on gray level cooccurrence matrix- (GLCM-) energy information for retinal vessel segmentation. Different 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. The 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 efficient with a higher average sensitivity and average accuracy rates in the same range of very good specificity.
authors: School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Durban 4000, South Africa.
authors: Temitope
authors: T
authors: Mapayi
authors: School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Durban 4000, South Africa.
authors: Serestina
authors: S
authors: Viriri
authors: School of Engineering, University of KwaZulu-Natal, Durban 4041, South Africa.
authors: Jules-Raymond
authors: JR
authors: Tapamo
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005189:False Positive Reactions; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009885:Ophthalmology / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D012372:ROC Curve; D015203:Reproducibility of Results; D012171:Retinal Vessels / Q000473:pathology*; D012984:Software