Abstract
Medical image processing has progressed in leaps and bounds with the advent of radical medical imaging modalities. Blood vessel segmentation from the retinal fundus image is very useful in the diagnosis of chronic vascular diseases, arteriosclerosis, diabetic retinopathy, hypertension, etc. This review paper aims to bring out the existing algorithms developed for the segmentation of vessels in the fundus. This paper covers various segmentation approaches categorised under template matching, multi-scale approach, region growing, active contour model and pattern recognition methods. Pattern recognition is further classified as unsupervised, supervised and deep learning approaches. Performance metrics such as accuracy, specificity, sensitivity, and area under the curve measures for these algorithms performed on the appropriate retinal databases are tabulated and discussed. Moreover, this paper discusses the impact of retinal blood vessel segmentation in screening cardiovascular and cerebrovascular diseases. Also, this paper recommends a universal blood vessel segmentation algorithm for the medical vasculature images.
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