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

This study explores the role of information theory in medical imaging, focusing on brain MRI analysis for tumor detection. By applying mutual information (MI) and statistical evaluation of pixel intensity distributions, the research demonstrates clear distinctions between normal and tumor-affected tissues. Tumor images exhibited higher MI values and concentrated intensity ranges, whereas normal images showed broader distributions. The results highlight the effectiveness of combining information-theoretic and statistical approaches to enhance diagnostic accuracy in neuroimaging and support the development of advanced analytical tools for healthcare applications.

• Understand the role of information-theoretic metrics in medical imaging:
Participants will be able to explain how mutual information and pixel intensity distributions can be applied to MRI analysis for differentiating between normal and tumor-affected brain tissues.

• Evaluate the practical implications of combining information theory with statistical analyses:
Participants will learn how these methods can enhance diagnostic accuracy in neuroimaging and support the development of computer-aided diagnostic systems for earlier and more reliable tumor detection.