Retinal blood vessel segmentation using saliency detection model and region optimization

In this paper, we present an algorithm for the effective segmentation of retinal blood vessels in vessel quantization for assessing the risk of cerebrovascular diseases. Given that the vessel is the highlight of the fundus image and has a characteristic texture, we adopt color and texture as the saliency features for vessel extraction combined with region optimization. The optimal thresholding can be obtained through the gray histogram thresholding method to segment the vessel. Moreover, morphological operators are applied to preserve the remaining small vessels considering the loss of small vessels. Experiments are designed to evaluate the performance of the proposed models with more than 94% accuracy. Experimental results reveal that the blood vessel can be effectively detected by applying our method on the retinal images.