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Automated Detection and Tortuosity Characterization of Retinal Vascular Networks

Automated retinal vascular network detection and analysis using digital retinal images continue to play a major role in the field of biomedicine for the diagnosis and management of various forms of human ailments like hypertension, diabetic retinopathy, retinopathy of prematurity, glaucoma and cardiovascular diseases. Although several literature have implemented different automatic approaches of detecting blood vessels in the retinal and also determining their tortuous states, the results obtained show that there are needs for further investigation on more efficient ways to detect and characterize the blood vessel network tortuosity states. This paper implements the use of an adaptive thresholding method based on local spatial relational variance (LSRV) for the detection of the retinal vascular networks. The suitability of a multi-layer perceptron artificial neural network (MLP-ANN) technique for the tortuosity characterization of retinal blood vascular networks is also presented in this paper. Some vessel geometric features of detected vessels are fed into ANN classifier for the automatic classification of the retinal vascular networks as being tortuous vessels or normal vessels. Experimental studies conducted on DRIVE and STARE databases show that the vascular network detection results obtained from the method implemented in this paper detects large and thin vascular networks in the retina. In comparison to preious methods in the literature, the proposed method for vascular network segmentation achieved better performance than several methods in the literature with a mean accuracy value of 95.04% and mean sensitivity value of 75.16% on DRIVE and mean accuracy value of 94.02% and average sensitivity value of 76.55% on STARE with computational processing time of 4.5 seconds and 9.4 seconds on DRIVE and STARE respectively. The MLP-ANN method proposed for the vascular network tortuosity characterization achieves promising accuracy rates of 77.5%, 80%, 83.33%, 85%, 86.67% and 100% for varying training sample sizes.