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.

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

MARINO, SILVIA;GRASSO, Giorgio Mario;PUCCIO, Luigia
;
BRAMANTI, Placido
2009-01-01

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.
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/1907005
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact