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.
News published in Agência FAPESP Newsletter about the scholarship: