Validation of Tsallis Entropy In Inter-Modality Neuroimage Registration

TL;DR

This study evaluates the use of Tsallis entropy within mutual information frameworks to improve the reliability of automated neuroimage registration across MRI, PET, and CT modalities.

Contribution

It introduces and tests Tsallis entropy as a novel alternative to Shannon entropy for mutual information-based image registration, demonstrating improved accuracy.

Findings

NMI and NMIT with Tsallis parameter near 1 perform best for CT-MRI and PET-MRI registration.

Tsallis entropy-based measures show higher confidence and accuracy in experiments.

The proposed method enhances the reliability of automated neuroimage registration.

Abstract

Medical image registration plays an important role in determining topographic and morphological changes for functional diagnostic and therapeutic purposes. Manual alignment and semi-automated software still have been used; however they are subjective and make specialists spend precious time. Fully automated methods are faster and user-independent, but the critical point is registration reliability. Similarity measurement using Mutual Information (MI) with Shannon entropy (MIS) is the most common automated method that is being currently applied in medical images, although more reliable algorithms have been proposed over the last decade, suggesting improvements and different entropies; such as Studholme et al, (1999), who demonstrated that the normalization of Mutual Information (NMI) provides an invariant entropy measure for 3D medical image registration. In this paper, we described a set of experiments to evaluate the applicability of Tsallis entropy in the Mutual Information (MIT) and in the Normalized Mutual Information (NMIT) as cost functions for Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and Computed Tomography (CT) exams registration. The effect of changing overlap in a simple image model and clinical experiments on current entropies (Entropy Correlation Coefficient - ECC, MIS and NMI) and the proposed ones (MIT and NMT) showed NMI and NMIT with Tsallis parameter close to 1 as the best options (confidence and accuracy) for CT to MRI and PET to MRI automatic neuroimaging registration.