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Development of a probabilistic atlas of 3D salient points automatically detected in magnetic resonance images with application to initial positioning of deformable geometric models

Grant number: 14/11988-0
Support type:Scholarships in Brazil - Master
Effective date (Start): September 01, 2014
Effective date (End): February 29, 2016
Field of knowledge:Engineering - Biomedical Engineering
Principal Investigator:Ricardo José Ferrari
Grantee:Carlos Henrique Villa Pinto
Home Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil

Abstract

The magnetic resonance imaging (MRI) has become an indispensable tool in the diagnosis and study of various diseases and syndromes of the central nervous system (CNS) as, for instance, multiple sclerosis and Alzheimer's disease. Besides the systematic visual analysis of MR images, the neuroradiologist often need to measure the volume or analyze changes in the shape of certain brain structures to enable rapid and accurate diagnosis of a disease, or even to perform the follow up of a particular treatment. For that, a prior segmentation of the structures of interest is required. Usually, this task is done manually and because of this has several limitations. For this reason, many researchers have turned their efforts to the development of automatic techniques for the segmentation of tissues and brain structures in MR images. Among the approaches proposed in the literature, the ones based on geometric deformable models using probabilistic and topological atlases are among the techniques presenting the best results. This is because they allow the use of anatomical information inherently contained in the meshes during the segmentation process. However, a major difficulty applying geometric deformable models for medical image segmentation is the proper initial positioning of the model. Thus, it is intended, for this research proposal, the improvement of a technique for automatic detection of 3D salient points and, from this, the development of a probabilistic atlas of salient points that will help to automate the initial positioning of deformable geometric models. Thus, segmentation techniques based on this approach may be more effective and will enable that volumetric measurements of brain structures are obtained with greater accuracy and speed.

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)
VILLA PINTO, CARLOS H.; FERRARI, RICARDO JOSE. Initialization of deformable models in 3D magnetic resonance images guided by automatically detected phase congruency point landmarks. PATTERN RECOGNITION LETTERS, v. 79, p. 1-7, AUG 1 2016. Web of Science Citations: 2.

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