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

Alessandro Celona, Pietro Lanzafame, Lilla Bonanno, Silvia Marino, Barbara Spanò, Giorgio Grasso, Luigia Puccio, Placido Bramanti

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. 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.

[DOI: 10.1685/CSC09283] About DOI

Keywords


Multiple Sclerosis, Magnetic Resonance Imaging, Artificial Neural Network based classification, Euclidean Distance Histogram

Full Text:



[DOI: 10.1685/] About DOI

Url Resolver: : http://dx.doi.org/10.1685/





Creative Commons License   Except where otherwise noted, content on this site is
  licensed under a Creative Commons 2.5 Italy License