Optimized deep knowledge-based no-reference image quality index for denoised MRI images

Abstract

The quality of the Magnetic Resonance Imaging (MRI) image influences the disease diagnosis and consequent treatment. However, noise distortion severely impacts these images and tends to interfere with diagnosis during the data acquisition/transmission. This contribution proposes a novel No-reference Image Quality Index (NIQI) method for the intelligent estimation of MRI images and to evaluate its efficacy compared to well-established approaches. A novel Optimized Deep Knowledge-based NIQI (ODK-NIQI) method is developed and tested rigorously. The ODK-NIQI method combines shuffle shepherd optimization and improved deep mish-activated ConvNet approach. The implementation of the projected approach is conducted in MATLAB software. The results demonstrate that the proposed method achieves the best performance and the highest consistency of objectives for both the noisy and denoised MRI brain images investigated. Additionally, the proposed method shows significant improvement over the traditional NIQI techniques using standard performance metrics comprising the Spearman's Rank Correlation Coefficient (SROCC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Pearson Linear Correlation Coefficient (PLCC). Overall, the proposed ODK-based NIQI strategy performs well in denoising MRI images.