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

Autores/as

  • Alessandro Celona IRCCS Centro Neurolesi ”Bonino Pulejo”, Messina, Italy
  • Pietro Lanzafame IRCCS Centro Neurolesi ”Bonino Pulejo”, Messina, Italy
  • Lilla Bonanno IRCCS Centro Neurolesi ”Bonino Pulejo”, Messina, Italy
  • Silvia Marino IRCCS Centro Neurolesi ”Bonino Pulejo”, Messina, Italy
  • Barbara Spanò IRCCS Centro Neurolesi ”Bonino Pulejo”, Messina, Italy
  • Giorgio Grasso Dipartimento di Fisica sez. Informatica, Università degli Studi di Messina, Italy
  • Luigia Puccio Università degli Studi di Messina, Italy
  • Placido Bramanti IRCCS Centro Neurolesi ”Bonino Pulejo”, Messina, Italy

DOI:

https://doi.org/10.1685/

Palabras clave:

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

Resumen

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

Biografía del autor/a

  • Luigia Puccio, Università degli Studi di Messina, Italy
    Dipartimanto di Matematica

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Publicado

2009-08-12

Número

Vol. 3 (2009)

Sección

Articles

Licencia

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