Multiple Sclerosis (MS) is an inflammatory demyelinating (that is, with myelin loss) disease of the Central Nervous System (CNS). It is considered an autoimmune disease which the immune system wrongly recognizes the myelin sheath of the CNS as an external element and attacks it, resulting in inflammation and formation of scars (sclerosis) in multiple areas of CNS's white matter substance. The multi-spectral imaging through magnetic resonance (MR) has been successfully used in diagnosing and monitoring MS due to its excellent properties such as high resolution and good differentiation between soft tissues. Nowadays, the preferred method to segment MS lesions is to manually delimit them in 3D MR images; this task is done by specialist with limited help of a computer. However, this approach is expensive and error prone between specialists, given that the lesions edges contrast is low. The big challenge in the automatic detection and segmentation of MS lesions in MR images is associated with the variability of size and location of lesions, low contrast due to partial volume effect and the high range of forms (highlighted, not highlighted, black holes) the lesions can assume depending on the stage of the disease. Recently, many researchers have turned their efforts to develop techniques that aim to reduce the amount of time spent on image analysis and to measure in a more precise way the volume of brain tissues and MS lesions. In this context, this project proposes the study and development of an automatic computational technique based on an outliers detection approach, Student's t-distribution finite mixture model and probabilistic atlases to detect and measure MS lesions volume in MR images.
News published in Agência FAPESP Newsletter about the scholarship: