@inproceedings{
    BioMed04-1,
    editor = "Tilg, B.",
    author = {Karp, Elina and G\"{a}vert, Hugo and S\"{a}rel\"{a}, Jaakko and Vig\'{a}rio, Ricardo},
    publisher = "ACTA Press",
    title = "Independent Component Analysis Decomposition of Structural MRI",
    booktitle = "Proceedings of the Second IASTED International Conference on Biomedical Engineering (BioMed 2004)",
    year = "2004",
    abstract = "The objective of this work was to create techniques for semi-automated analysis of magnetic resonance images of human brain, in order to provide better visualisation tools as well as detection and follow up of pathologies. The hypothesis was that each MR image can be considered as a linear combination of the contributions of the tis- sues present and therefore, independent component analy- sis (ICA) was thought to be advantageous as pre-processing technique in the analysis. The segmentation results for both simulated and real data obtained by clustering a self- organizing map, turned out to be promising. It was discov- ered that, in general, the data set including more images gave better results and ICA as pre-processing technique provided more accurate segmentations.",
    responsibleauthor = "Karp, Elina",
    internalpdf = "417-085.pdf",
    flags = "AIRC",
    address = "Innsbruck, Austria",
    keywords = "Medical imaging, image processing and signal processing, magnetic resonance imaging, independent component analysis, tissue segmentation",
    impactfactor = "D3",
    pages = "83--87"
}