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Research and development of automatic techniques for the detection, segmentation and analysis of multiple sclerosis plaques in magnetic resonance images

Grant number: 12/03100-3
Support type:Regular Research Grants
Duration: November 01, 2012 - October 31, 2014
Field of knowledge:Engineering - Biomedical Engineering
Principal researcher:Ricardo José Ferrari
Grantee:Ricardo José Ferrari
Home Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
Assoc. researchers: David Carlos Shigueoka ; Enedina Maria Lobato de Oliveira ; Henrique Carrete Junior ; Jose Roberto Falco Fonseca ; Nitamar Abdala ; Sergio Ajzen ; Vladimir Pekar

Abstract

Multiple Sclerosis (MS) is a complex multifactorial disease, demyelinating, autoimmune disease that affects the central nervous system and affects mainly young adults. It is considered an autoimmune disease because the immune system starts attacking the myelin sheath that covers neurons and compromises the function of the nervous system. The multimodal magnetic resonance imaging (MRI) has been very successfully used clinically for the diagnosis and monitoring of MS mainly due to the high resolution, good soft tissue differentiation, and for allowing the contrast of different information. The conventional method of measuring the volume of MS lesions is the manual delineation of lesions on MRI scans, performed by specialists with limited help from the computer. However, this procedure is arduous, time consuming, costly and is prone to large variability in inter-and intra-observer. Therefore, the main goal of this project is to develop computational techniques for automatic detection, measurement and analysis of the volume of MS plaques on MRI scans. (AU)

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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)
FREIRE, PAULO G. L.; FERRARI, RICARDO J. Automatic iterative segmentation of multiple sclerosis lesions using Student's t mixture models and probabilistic anatomical atlases in FLAIR images. COMPUTERS IN BIOLOGY AND MEDICINE, v. 73, p. 10-23, JUN 1 2016. Web of Science Citations: 4.
FERRARI, RICARDO J. Off-line determination of the optimal number of iterations of the robust anisotropic diffusion filter applied to denoising of brain MR images. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, v. 51, n. 1-2, p. 71-88, FEB 2013. Web of Science Citations: 10.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.