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Highly Robust Statistical Methods in Medical Image Analysis

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    0369205 - ÚI 2013 RIV PL eng J - Journal Article
    Kalina, Jan
    Highly Robust Statistical Methods in Medical Image Analysis.
    Biocybernetics and Biomedical Engineering. Roč. 32, č. 2 (2012), s. 3-16. ISSN 0208-5216
    R&D Projects: GA MŠk(CZ) 1M06014
    Institutional research plan: CEZ:AV0Z10300504
    Keywords : robust statistics * classification * faces * robust image analysis * forensic science
    Subject RIV: BB - Applied Statistics, Operational Research
    Impact factor: 0.208, year: 2012

    Standard multivariate statistical methods in medical applications are too sensitive to the assumption of multivariate normality and the presence of outliers in the data. This paper is devoted to robust statistical methods. In the context of medical image analysis they allow to solve the tasks of face detection and face recognition in a database of images. The results of the robust approaches in image analysis turn out to outperform those obtained with standard methods. Robust methods also have desirable properties appealing for practical applications, including dimension reduction and clear interpretability.
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