Information-Theoretic Analysis of Brain MRI: Mutual Information and Pixel Intensity Patterns in Tumor vs. Normal Tissues

TL;DR

This paper demonstrates that information-theoretic measures like mutual information and pixel intensity distributions can effectively differentiate tumor from normal brain tissues in MRI images, aiding diagnostic processes.

Contribution

The study introduces the application of mutual information and pixel intensity analysis to distinguish tumor from normal brain MRI images, revealing distinct structural patterns.

Findings

Higher mutual information in tumor images

Distinct pixel intensity distribution patterns between groups

Predictable structural characteristics in tumor tissues

Abstract

The application of information theory in medical imaging, particularly in magnetic resonance imaging (MRI), offers powerful quantitative tools for analyzing structural differences in brain tissues. This study utilizes mutual information (MI) and pixel intensity distributions to differentiate between normal and tumor-affected brain MRI images. Mutual information analyses revealed significantly higher MI values in tumor images compared to normal ones, indicating greater internal similarity within tumor images. Pixel intensity analysis further demonstrated distinct distribution patterns between the two groups: tumor images showed pronounced pixel frequency concentrations within a specific intensity range (0.3, 0.4), suggesting predictable structural characteristics. Conversely, normal images exhibited broader, more uniform pixel intensity distributions across most intensity ranges, except for an initial peak observed in both groups. These findings highlight the capability of information-theoretic metrics, such as mutual information and pixel intensity analysis, to effectively distinguish tumor tissue from normal brain structures, providing promising avenues for enhanced diagnostic and analytical methods in neuroimaging.