Brain tumor pathological area delimitation through Non-negative Matrix Factorization

Sandra Ortega-Martorell*, Paulo J.G. Lisboa, Alfredo Vellido, Rui V. Simões, Margarida Julià-Sapé, Carles Arús

*Corresponding author for this work

Research output: Other contribution


Pattern Recognition and Data Mining can provide invaluable insights in the field of neuro oncology. This is because the clinical analysis of brain tumors requires the use of non-invasive methods that generate complex data in electronic format. Magnetic resonance, in the modalities of imaging and spectroscopy, is one of these methods that has been widely applied to this purpose. The heterogeneity of the tissue in the brain volumes analyzed by magnetic resonance remains a challenge in terms of pathological area delimitation. In this brief paper, we show that the Convex-Nonnegative Matrix Factorization technique can be used to extract MRS signal sources that are extremely tissue type-specific and that can be used to delimit these pathological areas with great accuracy.

Original languageAmerican English
Number of pages6
Publication statusPublished - 2011

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


  • Brain tumors
  • Magnetic resonance spectroscopy imaging
  • Nonnegative matrix factorization


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