Medical image registration using TSallis Entropy in Statistical Parametric Mapping (SPM)

The superposition of medical images, technically known as co-registration, can take a major role in determining the topographic and morphological changes in functional diagnostic and therapeutic purposes. This paper describes a study focused on to find an alternative cost function method for medical images co-registration through the study of performance and robustness of the TSallis Entropy in Statistical Parametric Mapping package (SPM). Images of Magnetic Resonance (MR) and Single Photon Emission Computed Tomography (SPECT) of 3 patients morphologically normal were used for the construction of anatomic phantoms containing predetermined geometric variations. The simulated images were co-registered with the original images using traditional techniques and the proposed method. The comparative analysis of the Root Mean Square (RMS) error showed that the Tsallis Entropy was more efficient in the intramodality alignment, while the Shannon Entropy in the intermodality one; revealing therefore the importance of the implementation of the Tsallis Entropy in SPM for applications in neurology and neuropsychiatric evaluation.