Artificial Neural Network (ANN) Morphological Classification by Euclidean Distance Histograms for Prognostic Evaluation of Magnetic Resonance Imaging in Multiple Sclerosis

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. This work focuses on the application of Artificial
Neural Network (ANN) based classification of MS lesions, to monitor evolution in time of
lesions and to correlate this to MS phases. An euclidean distance histogram, representing
the distribution of edge inter-pixel distances, is used as input. This technique gives a very
promising recognition rate.