Abstract
AIM: To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features. METHODS: A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization (NICLAHE) to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures. Additionally, a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures. The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm, but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values. It was evaluated on the Drive and high-resolution fundus (HRF) datasets on conventional quality measures. RESULTS: The new proposed preprocessing technique was applied to two retinal image databases, Drive and HRF, with four quality metrics being, root mean square error (RMSE), peak signal to noise ratio (PSNR), root mean square contrast (RMSC), and overall contrast. The technique performed superiorly on both the data sets as compared to the traditional enhancement methods. In order to assess the compatibility of the method with automated diagnosis, a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels. Sensitivity, specificity, precision and accuracy were used to analyse the performance. NICLAHE-enhanced images outperformed the traditional techniques on both the datasets with improved accuracy. CONCLUSION: NICLAHE provides better results than traditional methods with less error and improved contrast-related values. These enhanced images are subsequently measured by sensitivity, specificity, precision, and accuracy, which yield a better result in both datasets.