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Validation of Tsallis entropy in neuroimage registration

Grant number: 13/08240-0
Support type:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): September 05, 2013
Effective date (End): August 04, 2014
Field of knowledge:Engineering - Biomedical Engineering
Principal Investigator:Paulo Mazzoncini de Azevedo Marques
Grantee:Henrique Tomaz Do Amaral Silva
Supervisor abroad: Colin Studholme
Home Institution: Faculdade de Medicina de Ribeirão Preto (FMRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
Local de pesquisa : University of Washington, United States  
Associated to the scholarship:10/10979-6 - Clinical validation of use of Tsallis entropy in neuroimage co-registration for the location of the epileptogenic zone using the Statistical Parametric Mapping (SPM), BP.DR


Medical image registration plays an important role in determining topographic and morphological changes for functional diagnostic and therapeutic purposes. Manual alignment and semi-automated softwares 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, who in a previous study, demonstrated that the normalization of Mutual Information (NMI) provides an invariant entropy measure for 3D medical image registration [Studholme et. al., 1999]. It is necessary to extend the classical theory presented by Shannon when analyzing certain classes of physical systems that possess long range interactions, long time memories and fractal-type structure. The relevance of fractal geometry in medical image processing is explained by the self-similarity observed in biological structures imaged with a finite resolution. Inspired by multifractal concept, Constantino Tsallis proposed a generalization of Boltzmann-Gibbs-Shannon (BGS) statistics based on the entropy of generalized formulism. In this project we describe a set of experiments to evaluate the applicability of Tsallis entropy in the Mutual Information (MIT) as a cost function for Positron Emission Tomography (PET) and Computed Tomography (CT) registration. The effect of changing overlap on current entropies (MIS and NMI) and the proposed measurement (MIT) will be compared using a simple image model and experiments on clinical image data, as proposed by Colin Studholme, cited previously. (AU)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
AMARAL-SILVA, HENRIQUE; WICHERT-ANA, LAURO; MURTA, LUIZ OTAVIO; ROMUALDO-SUZUKI, LARISSA; ITIKAWA, EMERSON; BUSSATO, GERALDO FILHO; AZEVEDO-MARQUES, PAULO. The Superiority of Tsallis Entropy over Traditional Cost Functions for Brain MRI and SPECT Registration. Entropy, v. 16, n. 3, p. 1632-1651, MAR 2014. Web of Science Citations: 6.

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