Analysis and Comparison of Image Enhancement Technique for Improving PSNR of Lung Images by Median Filtering over Histogram Equalization Technique

Abstract

Aim: The main goal of this project is image enhancement to improve interpretability or perception of information in images for human viewers and also to provide better input for other automated image processing techniques. Materials and Methods: In this research different sources of lung images collected from Kaggle website were used. Samples were considered as (N=30) for median filtering and (N=30) for novel histogram equalization technique with total sample size calculated using clinical.com. As a result the total number of sample was calculated to be 60.Using SPSS Software and a standard data set,the PSNR was obtained. Both median filter and novel histogram technique image enhancement were implemented on Lung images through Matlab coding and also extracting PSNR values of each image. Then through SPSS software comparison and analysis has been made Results: In an image enhancement of the image processing pathway, novel histogram equalization technique shows the best performance by removing noise to improve PSNR of lung images than median filtering. Comparison of PSNR values are done by independent sample test using IBM-SPSS software. There is a statistical difference between histogram technique and median filtering. The novel histogram equalization technique showed higher results of PSNR (69.6557dB) with (p=0.04) in comparison with median filtering (37.6427dB). Conclusion: Histogram equalization image enhancement technique provides high PSNR values for different sources of lung images than median filtering Technique.