@MASTERSTHESIS\{IMM2014-06790,
    author       = "J. B. S{\o}rensen",
    title        = "Support Vector Machines for Pixel Classification - Application in Microscopy Images",
    year         = "2014",
    school       = "Technical University of Denmark, Department of Applied Mathematics and Computer Science",
    address      = "Richard Petersens Plads, Building 324, {DK-}2800 Kgs. Lyngby, Denmark, compute@compute.dtu.dk",
    type         = "",
    note         = "{DTU} Supervisor: Lars Kai Hansen, lkai@dtu.dk, {DTU} Compute",
    url          = "http://www.compute.dtu.dk/English.aspx",
    abstract     = "Visiopharm currently uses Bayesian classification and {K-}means clustering for pixel classification in their software. The purpose of this project was to investigate if Support Vector Machines (SVMs) would be a good additional classifier. It will be shown that a quantitative improvement (increase in accuracy) is indeed possible compared to existing methods, but that this is not the only thing to take into consideration. Overall SVMs does seem like a good addition to Visiopharms software, but more projects should follow this, to answer some of the new questions which this project has raised."
}