A novel chaotic weighted EHO-based methodology for retinal vessel segmentation

Abstract

ABSTRACT Retinal image segmentation process deals withproblems like spurious vascularisation and thin vessel detection. In thispaper, a three-step methodology has been proposed for retinal vesselsegmentation. In first step, RGB to YIQ conversion is performed. In second step, Y component enhancement is performed. A novel Chaotic weighted Elephant HerdingOptimization (CWEHO) has been proposed to optimize the clip limit and blocksize values of Contrast Limited Adaptive Histogram Equalization (CLAHE). CWEHO-based CLAHE along with morphological operations, non-local means filter, and median filter is applied to enhanceretinal images. In third step, thin and thick vessel segmentation is performed.Top hat transformation, otsu thresholding algorithm, and vessel point selectionare applied for thick vessel extraction. The first-order Gaussian derivative in conjunction with the matchfilter is used to extract thin vessels. DRIVE and HRF datasets are used toassess the effectiveness of proposed methodology. The average values ofsegmentation accuracy, specificity, sensitivity, and Mathew CorrelationCoefficient (MCC) are observed to be 0.9650, 0.9895, 0.7007, 0.7650,respectively, for observer1 and 0.9696, 0.9912, 0.7390, 0.7901 for observer2using DRIVE dataset. Similarly, 0.9592, 0.9839, 0.6850, and 0.7116,respectively, metrics for HRF dataset. Compared to state-of-the-art methods, the proposed segmentation methodology providesbetter results .