Abstract
Multiple Sclerosis (MS) is an autoimmune condition in which the immune system attacks the Central Nervous System. Magnetic Resonance Imaging (MRI) is today a crucial tool for diagnosis of MS by allowing in-vivo detection of lesions. New lesions may represent new inflammation; they may increase in size during acute phase to contract later while the disease severity is reduced. To monitor evolution in time of lesions and to correlate this to MS phases, we focused on the application of Artificial Neural Network (ANN) based classification of MS lesions. An euclidean distance histogram, representing the distribution of edge inter-pixel distances, is used as input. In this work, we have extended the study already published, increasing to 21 the number of images. We can observe that the percentage of correct results on 21 images (93.81%) increased if compared to the study performed on 13 images (92.31%). This methodology could be used to monitor evolution in time of lesions of each patient and to correlate this to MS phases (i.e. to know if the lesions change their form).
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Bramanti, A., Bonanno, L., Bramanti, P., Lanzafame, P. (2013). Artificial Neural Network (ANN) Morphological Classification of Magnetic Resonance Imaging in Multiple Sclerosis. In: Apolloni, B., Bassis, S., Esposito, A., Morabito, F. (eds) Neural Nets and Surroundings. Smart Innovation, Systems and Technologies, vol 19. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35467-0_13
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DOI: https://doi.org/10.1007/978-3-642-35467-0_13
Publisher Name: Springer, Berlin, Heidelberg
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