Discriminant Convex Non-negative Matrix Factorization for the classification of human brain tumours

Albert Vilamala, Paulo J.G. Lisboa, Sandra Ortega-Martorell, Alfredo Vellido

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    13 Citations (Scopus)


    The medical analysis of human brain tumours commonly relies on indirect measurements. Among these, magnetic resonance imaging (MRI) and spectroscopy (MRS) predominate in clinical settings as tools for diagnostic assistance. Pattern recognition (PR) methods have successfully been used in this task, usually interpreting diagnosis as a supervised classification problem. In MRS, the acquired spectral signal can be analyzed in an unsupervised manner to extract its constituent sources. Recently, this has been successfully accomplished using Non-negative Matrix Factorization (NMF) methods. In this paper, we present a method to introduce the available class information into the unsupervised source extraction process of a convex variant of NMF. Novel techniques to generate diagnostic predictions for new, unseen spectra using the proposed Discriminant Convex-NMF are also described and experimentally assessed. © 2013 Elsevier B.V. All rights reserved.
    Original languageEnglish
    Pages (from-to)1734-1747
    JournalPattern Recognition Letters
    Issue number14
    Publication statusPublished - 1 Jan 2013


    • Brain tumours
    • Discriminant Convex Non-negative Matrix
    • Factorization
    • Magnetic resonance spectroscopy
    • Source separation


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