Efficient retinal vessel detection using line detectors with morphological operations

Abstract

Digital fundus photography plays a major role in the diagnosis of different retinal pathologies like hypertension, diabetic retinopathy and Glaucoma. To identify abnormal components on the retina, retinal features should be detected accurately. Retinal vessel structure is one of the important landmarks of the retina. So precise detection of retinal vessel structure is imperative. This paper presents a simple, robust retinal vessel extraction approach based on the line detectors and morphological operations. As vessel detection is basically a problem of a line detection, the green channel retinal image is applied to morphological opening using a line as structuring element. The resultant image is again applied with the line detectors and thresholded using Otsu’s thresholding. The proposed algorithm overcomes the fundamental issues of scale and orientation avoiding the need of multiple thresholds with improved values of performance measure as compared to the state of the art techniques. The proposed algorithm is applied on 3 standard databases-HRF (healthy and Diabetic), DIARETDB1 and DRIVE. Area under the ROC curve (AUC) of 97% was achieved with 91% Sensitivity and 97% Specificity for DRIVE dataset. The proposed algorithm achieved an Accuracy of 97%, Sensitivity of 85 % and Specificity of 97% for HRF database. On DIARETDB1 database too observed very good results.