Luiz Otávio Murta

Vinicius Pavanelli Vianna, Luiz Otavio Murta

Image registration is a key operation in medical image processing, allowing a plethora of applications. Mutual information (MI) is consolidated as a robust similarity metric often used for medical image registration. Although MI provides a robust medical image registration, it usually fails when the needed image transform is too big due to MI local maxima traps. In this paper, we propose and evaluate a generalized parametric MI as an affine registration cost function. We assessed the generalized MI (GMI) functions for separable affine transforms and exhaustively evaluated the GMI mathematical image seeking the maximum registration range through a gradient descent simulation. We also employed Monte Carlo simulation essays for testing translation registering of randomized T1 versus T2 images. GMI functions showed to have smooth isosurfaces driving the algorithm to the global maxima. Results show significantly prolonged registration ranges, avoiding the traps of local maxima. We evaluated a range of [-150mm,150mm] for translations, [-180°,180°] for rotations, [0.5,2] for scales, and [-1,1] for skew with a success rate of 99.99%, 97.58%, 99.99%, and 99.99% respectively for the transforms in the simulated gradient descent. We also obtained 99.75% success in Monte Carlo simulation from 2,000 randomized translations trials with 1,113 subjects T1 and T2 MRI images. The findings point towards the reliability of GMI for long-range registration with enhanced speed performance

Henrique Tomaz Amaral-Silva, Luiz Otavio Murta-Jr, Paulo Mazzoncini de Azevedo-Marques et al.

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.