| Grant number: | 13/08240-0 |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| Start date: | September 05, 2013 |
| End date: | August 04, 2014 |
| Field of knowledge: | Engineering - Biomedical Engineering - Bioengineering |
| Principal Investigator: | Paulo Mazzoncini de Azevedo Marques |
| Grantee: | Henrique Tomaz Do Amaral Silva |
| Supervisor: | Colin Studholme |
| Host Institution: | Faculdade de Medicina de Ribeirão Preto (FMRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil |
| Institution abroad: | 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 |
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 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. | |
| News published in Agência FAPESP Newsletter about the scholarship: | |
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