@inproceedings{
    BioMed04-2,
    editor = "Tilg, B.",
    author = "Karp, Elina and Vig\'{a}rio, Ricardo",
    publisher = "ACTA Press",
    title = "Unsupervised MRI Tissue Classification by Support Vector Machines",
    booktitle = "Proceedings of the Second IASTED International Conference on Biomedical Engineering (BioMed 2004)",
    year = "2004",
    abstract = "The objective of this work was to develop better visuali- sation tools and techniques for detection and follow up of pathologies in magnetic resonance images. Support vector machines were used, in an unsupervised manner, to seg- ment tissues in MR images with different imaging param- eters. The segmentation rested on a training set of labelled feature vectors defined using independent component anal- ysis. Both simulated and real data was used. Support vector machines proved to be a suitable tool for classification of MR images. The classification error rates for the simulated data indicated that rather good segmentation precision was achieved.",
    responsibleauthor = "Karp, Elina",
    internalpdf = "417-086.pdf",
    flags = "AIRC",
    address = "Innsbruck, Austria",
    keywords = "Medical imaging, image processing and signal processing, magnetic resonance imaging, support vector machines, unsupervised classification",
    impactfactor = "D3",
    pages = "88--91"
}