Detection and comparison of Diabetic Maculopathy using C-Means Clustering Algorithm and Watershed Algorithm

Abstract

Aim: The aim of this research work is for the presence of Novel Diabetic Maculopathy Detection using modern algorithms, and comparing the Peak Signal to Noise Ratio (PSNR) between the C-Means clustering Algorithms and Watershed Algorithm. Materials and Methods: The sample images were taken from kaggle’s website. Samples were considered as (N=24) for C-Means Clustering Algorithm and (N=24) for Watershed 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 Peak Signal to Noise Ratio was calculated by using the MATLAB Programming with a standard data set. Results: Comparison of PSNR is done by independent sample t-test using SPSS software. There is a statistical insignificant difference between C-Means Clustering Algorithm and Watershed algorithm with p=0.11, p>0.05 (PSNR = 35.3411) showed better results in comparison to Watershed Algorithm (PSNR =9.7420). Conclusion: C-Means Clustering Algorithms were found to give higher PSNR than in Watershed Algorithms for the Novel Diabetic Maculopathy Detection.