Automatic lesion incidence estimation and detection in multiple sclerosis using multisequence longitudinal MRI Academic Article uri icon

Overview

MeSH Major

  • Algorithms
  • Brain Diseases
  • Image Interpretation, Computer-Assisted
  • Magnetic Resonance Imaging
  • Multiple Sclerosis
  • Nerve Fibers, Myelinated
  • Pattern Recognition, Automated

abstract

  • This fully automated and computationally fast method allows sensitive and specific detection of lesion incidence that can be applied to large collections of images. Using the explicit form of the statistical model, SuBLIME can easily be adapted to cases when more or fewer imaging sequences are available.

publication date

  • January 2013

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC3554794

Digital Object Identifier (DOI)

  • 10.3174/ajnr.A3172

PubMed ID

  • 22766673

Additional Document Info

start page

  • 68

end page

  • 73

volume

  • 34

number

  • 1