@mastersthesis{
    Goncalves2006-MSCTHESIS,
    author = "Gonçalves, Nicolau",
    school = "Helsinki University of Technology",
    responsibleperson = "Nicolau Gon\c{c}alves",
    title = "Exploratory Analysis of {MRI}: a tissue segmentation study",
    timestamp = "2009.05.28",
    abstract = "Magnetic resonance imaging (MRI) is a widely used non-invasive diagnostic tool, with excellent spatial resolution, and efficient in distinguishing between soft tissues. The assessment of the severity of brain lesions requires expert evaluation of MR images. In this thesis, a method is presented for semi-automatic detection of those lesions. The goal is to provide a reliable tool for lesion detection, by improving tissue contrast and visualisation. The final objective is to reduce the intensive need for specialists, and allowing a rather systematic follow-up of the lesion evolution, or its treatment. The approach taken is based on a combination of machine learning techniques. First, independent component analysis is used to allow for tissue segmentation. Afterwards, self-organising maps are applied, in order to create labels for the subsequent tissue classification. Then, two classification methods are experimented: support vector machines and discriminative clustering, with special enfasis on the latter.",
    address = "Espoo, Finland",
    month = "July",
    owner = "ngoncalv",
    flags = "public, MSCTHESIS",
    file = "mscthesis.pdf:mscthesis.pdf:PDF",
    year = "2006",
    keywords = "Magnetic resonance imaging, independent component analysis, self-organising maps, principal component analysis, medical imaging, image and signal processing, tissue segmentation, unsupervised classification, clustering, support vector machines.",
    type = "Master's Thesis"
}