Abstract
Retinal blood vessels have been presented to contribute confirmation with regard to tortuosity, branching angles, or change in diameter as a result of ophthalmic disease. Although many enhancement filters are extensively utilized, the Jerman filter responds quite effectively at vessels, edges, and bifurcations and improves the visualization of structures. In contrast, curvelet transform is specifically designed to associate scale with orientation and can be used to recover from noisy data by curvelet shrinkage. This paper describes a method to improve the performance of curvelet transform further. A distinctive fusion of curvelet transform and the Jerman filter is presented for retinal blood vessel segmentation. Mean-C thresholding is employed for the segmentation purpose. The suggested method achieves average accuracies of 0.9600 and 0.9559 for DRIVE and CHASE_DB1, respectively. Simulation results establish a better performance and faster implementation of the suggested scheme in comparison with similar approaches seen in the literature.
Highlights
Evaluation of the physical features of the retinal vascular structure can create understanding of the pathological transformation generated by ocular diseases
The suggested method is a unique combination of a Jerman filter with curvelet transform to improve the performance measures of blood vessels
The DRIVE database images are divided into two sets—training data set and testing data set
Summary
Evaluation of the physical features of the retinal vascular structure can create understanding of the pathological transformation generated by ocular diseases. Blood vessels are dominating and mainly steady structures, which appear in the retina that is detected directly in vivo. The efficacy of cure for ophthalmologic disorders is dependent on the prompt recognition of alteration in retinal pathology. The manual labelling of retinal blood vessels is a tedious procedure that requires training and skill. Computerized segmentation offers reliability and accuracy and decreases the consumption of time by a physician or a skilled technician for hand mapping. An automatic definitive approach of vessel segmentation would be beneficial for the prior recognition and characterization of morphological alterations in the retinal vasculature. The feature representation and extraction in retinal images is a difficult assignment. The foremost complications are the lighting changes, insufficient contrast, noise effect, and anatomic changeability dependent on the individual patient
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