A New Local Fractional Entropy-Based Model for Kidney MRI Image Enhancement.

Area of Science:

• Medical Imaging
• Image Processing
• Biomedical Engineering

Background:

• Kidney image enhancement is difficult due to variable MRI quality and kidney disease characteristics.
• Existing methods struggle to adequately improve the clarity of low-quality kidney images.

Purpose of the Study:

• To propose and evaluate a novel Local Fractional Entropy (LFE)-based model for enhancing kidney MRI images.
• To improve the visibility of fine details in poor-quality kidney images.

Main Methods:

• Developed a new Local Fractional Entropy (LFE) model.
• The model estimates edge probabilities using neighboring pixel entropy.
• Tested on a large dataset of diverse, low-quality kidney images.

Main Results:

• The LFE model effectively enhances fine image details by analyzing local pixel intensity changes.
• Demonstrated utility and effectiveness on a large dataset of challenging kidney images.
• Outperformed classical and recent image enhancement techniques.

Conclusions:

• The proposed Local Fractional Entropy (LFE) model is a powerful tool for kidney image enhancement.
• This method offers significant improvements over existing techniques for improving diagnostic image quality.