Support Vector Machines for Pixel Classification - Application in Microscopy Images

Jakob Busk Sørensen

AbstractVisiopharm 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.
TypeMaster's thesis [Industrial collaboration]
Year2014
PublisherTechnical University of Denmark, Department of Applied Mathematics and Computer Science
AddressRichard Petersens Plads, Building 324, DK-2800 Kgs. Lyngby, Denmark, compute@compute.dtu.dk
NoteDTU Supervisor: Lars Kai Hansen, lkai@dtu.dk, DTU Compute
Electronic version(s)[pdf]
Publication linkhttp://www.compute.dtu.dk/English.aspx
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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