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A CNN Based Algorithm for the Automated Segmentation of Multiple Sclerosis Lesions

  • Eleonora Bilotta
  • Antonio Cerasa
  • Pietro Pantano
  • Aldo Quattrone
  • Andrea Staino
  • Francesca Stramandinoli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)

Abstract

We describe a new application based on genetic algorithms (GAs) that evolves a Cellular Neural Network (CNN) capable to automatically determine the lesion load in multiple sclerosis (MS) patients from Magnetic Resonance Images (MRI). In particular, it seeks to identify in MRI brain areas affected by lesions, whose presence is revealed by areas of lighter color than the healthy brain tissue. In the first step of the experiment, the CNN has been evolved to achieve better performances for the analysis of MRI. Then, the algorithm was run on a data set of 11 patients; for each one 24 slices, each with a resolution of 256 ×256 pixels, were acquired. The results show that the application is efficient in detecting MS lesions. Furthermore, the increased accuracy of the system, in comparison with other approaches, already implemented in the literature, greatly improves the diagnosis for this disease.

Keywords

Cellular Neural Networks Genetic Algorithms Automated Magnetic Resonance Imaging Analysis Multiple Sclerosis lesion load 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Eleonora Bilotta
    • 1
  • Antonio Cerasa
    • 2
  • Pietro Pantano
    • 1
  • Aldo Quattrone
    • 2
  • Andrea Staino
    • 1
  • Francesca Stramandinoli
    • 1
  1. 1.Evolutionary Systems GroupUniversity of CalabriaCosenzaItaly
  2. 2.Neuroimaging Research Unit, Institute of Neurological SciencesNational Research CouncilCatanzaroItaly

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