Detection and comparison of Proliferative Diabetic Retinopathy using Watershed Algorithm and K-Means Clustering Algorithm

Abstract

Aim: The aim of this research work is for the presence of Innovative Proliferative Diabetic Retinopathy Detection, using modern algorithms, and comparing the Peak Signal to Noise Ratio (PSNR) between Watershed Algorithms and K-Means Clustering Algorithm. Materials and methods: The sample images were taken from kaggle’s website. Samples were considered as (N=24) for Watershed Algorithm and (N=24) for K-means clustering algorithm in accordance with total sample size calculated using clinicalc.com by keeping alpha error-threshold value 0.05, enrollment ratio as 0.1, 95% confidence interval, G power as 80%. The PSNR was calculated by using the MATLAB Programming with a standard data set. Results: Comparison of PSNR is done by independent sample test using SPSS software. There is a statistical significant difference between Watershed Algorithm and K-means clustering algorithm with p<0.001, p<0.05 (PSNR=10.8205) using Watershed Algorithm showed better results in comparison to K-Means Clustering Algorithm (PSNR=9.7350). Conclusion: Watershed Algorithms were found to give higher PSNR than in K-Means Clustering Algorithms for the Innovative Proliferative Diabetic Retinopathy Detection.