title: A MS-lesion pattern discrimination plot based on geostatistics.
abstract: A geostatistical approach to characterize MS-lesion patterns based on their geometrical properties is presented.
  
  
  
  A dataset of 259 binary MS-lesion masks in MNI space was subjected to directional variography. A model function was fit to express the observed spatial variability in x, y, z directions by the geostatistical parameters Range and Sill.
  
  
  
  Parameters Range and Sill correlate with MS-lesion pattern surface complexity and total lesion volume. A scatter plot of ln(Range) versus ln(Sill), classified by pattern anisotropy, enables a consistent and clearly arranged presentation of MS-lesion patterns based on geometry: the so-called MS-Lesion Pattern Discrimination Plot.
  
  
  
  The geostatistical approach and the graphical representation of results are considered efficient exploratory data analysis tools for cross-sectional, follow-up, and medication impact analysis.
authors: Interfaculty Department of Geoinformatics Z_GIS Univ. Salzburg Schillerstr. 305020 Salzburg Austria; Department of Neurology Christian Doppler Medical Centre Paracelsus Medical University Ignaz Harrer-Straße 795020 Salzburg Austria.
authors: Robert
authors: R
authors: Marschallinger
authors: Department of Neurology Klinikum rechts der Isar Technische Universität München Munich Germany; TUM-Neuroimaging Center Klinikum rechts der Isar Technische Universität München Munich Germany; Department of Statistics Ludwig-Maximilians-University München Munich Germany.
authors: Paul
authors: P
authors: Schmidt
authors: Interfaculty Department of Geoinformatics Z_GIS Univ. Salzburg Schillerstr. 305020 Salzburg Austria; Department of Neurology Christian Doppler Medical Centre Paracelsus Medical University Ignaz Harrer-Straße 795020 Salzburg Austria.
authors: Peter
authors: P
authors: Hofmann
authors: Department of Neuroradiology Klinikum rechts der Isar Technische Universität München Munich Germany.
authors: Claus
authors: C
authors: Zimmer
authors: Faculty of Science and Technology Lancaster University Engineering Building Lancaster LA1 4YR UK; Faculty of Geosciences University of Utrecht Heidelberglaan 23584 CS Utrecht The Netherlands; School of Geography, Archaeology and Palaeoecology Queen's University Belfast Belfast BT7 1NN Northern Ireland UK.
authors: Peter M
authors: PM
authors: Atkinson
authors: Department of Neurology Christian Doppler Medical Centre Paracelsus Medical University Ignaz Harrer-Straße 795020 Salzburg Austria; Department of Neurology Klinikum rechts der IsarTechnische Universität München Munich Germany.
authors: Johann
authors: J
authors: Sellner
authors: Department of Neurology Christian Doppler Medical Centre Paracelsus Medical University Ignaz Harrer-Straße 79 5020 Salzburg Austria.
authors: Eugen
authors: E
authors: Trinka
authors: Department of Neurology Klinikum rechts der Isar Technische Universität München Munich Germany; TUM-Neuroimaging Center Klinikum rechts der Isar Technische Universität München Munich Germany; Munich Cluster for Systems Neurology (SyNergy) Munich Germany.
authors: Mark
authors: M
authors: Mühlau
mesh_terms: D003198:Computer Simulation; D003430:Cross-Sectional Studies; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods / Q000706:statistics & numerical data*; D009103:Multiple Sclerosis / Q000000981:diagnostic imaging* / Q000453:epidemiology / Q000503:physiopathology; D010363:Pattern Recognition, Automated / Q000379:methods*; D010865:Pilot Projects
keywords: Discrimination; Multiple Sclerosis; geostatistics; lesion; pattern