Adaptive Thresholding Technique for Retinal Vessel Segmentation Based on GLCM-Energy Information

Abstract EN

Although retinal vessel segmentation has been extensively researched, a robust and time efficientsegmentation method is highly needed.

This paper presents a local adaptive thresholding techniquebased on gray level cooccurrence matrix- (GLCM-) energy information for retinal vessel segmentation.

Different thresholds were computed using GLCM-energy information.

An experimentalevaluation on DRIVE database using the grayscale intensity and Green Channel of the retinal imagedemonstrates the high performance of the proposed local adaptive thresholding technique.

The maximumaverage accuracy rates of 0.9511 and 0.9510 with maximum average sensitivity rates of 0.7650and 0.7641 were achieved on DRIVE and STARE databases, respectively.

When compared to thewidely previously used techniques on the databases, the proposed adaptive thresholding technique istime efficient with a higher average sensitivity and average accuracy rates in the same range of verygood specificity.