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Research and development of automatic techniques for detection and volume measurement of multiple sclerosis plaques


Multiple sclerosis (MS) is a inflammatory and demyelinating disease of the central nervous system (CNS) which affects mainly young adults. It is regarded to be an autoimmune disorder, wherein the immune system mistakenly recognizes CNS myelin as "foreign" and attacks it, resulting in inflammation and damage. Although the factors triggering the pathological change in MS remain poorly understood due to the complexity of the alterations, in the past few years, new therapies have been proposed for the treatment of this disease. Multimodal magnetic resonance imaging has been used clinically with great success for the diagnosis and monitoring of MS due to the high resolution, good soft tissue differentiation, and different contrast information. The conventional method to measure volumes of MS lesions is to delineate the lesions manually by experts with some extent of computer assistance. However such procedure is arduous, time consuming, costly, and prone to a large inter- and intra-observer variability. Therefore, the main goal of this project is the research and development of automatic techniques for the detection and volume measurement of MS plaques in MR images, aiming to decrease variability measures and to allow automatic and quantitative analysis of the MS. The development of such techniques will help in following the evolution of this pathology and to better detect possible therapeutic effects in clinical trials. In addition, this project aims to create and to establish a group of excellence in the area of medical image processing with emphasis in the study of brain diseases. (AU)

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(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)
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, . (08/09050-2, 12/03100-3)

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