Brain tumor pathological area delimitation through Non-negative Matrix Factorization Conference Paper
| Authors: | Ortega-Martorell, S.; Lisboa, P. J. G.; Vellido, A.; Simões, R. V.; Julià-Sapé, M.; Arús, C. |
| Title: | Brain tumor pathological area delimitation through Non-negative Matrix Factorization |
| Conference Title: | 11th IEEE International Conference on Data Mining Workshops |
| Abstract: | 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. © 2011 IEEE. |
| Keywords: | brain; tumors; magnetic resonance; magnetic resonance spectroscopy; signal processing; brain tumors; pattern recognition; nonnegative matrix factorization; neuro-oncology; data mining; magnetic resonance spectroscopy imaging; brain volume; clinical analysis; complex data; electronic formats; matrix factorizations; noninvasive methods; signal source; blind source separation; noninvasive medical procedures |
| Journal Title | Proceedings of the IEEE 11th International Conference on Data Mining Workshops |
| Conference Dates: | 2011 Dec 11 |
| Conference Location: | Vancouver, Canada |
| ISBN: | 978-0-7695-4409-0 |
| Publisher: | IEEE |
| Date Published: | 2011-01-01 |
| Start Page: | 1058 |
| End Page: | 1063 |
| Language: | English |
| DOI: | 10.1109/ICDMW.2011.41 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | --- - Proceedings - IEEE International Conference on Data Mining, ICDM - Proc. IEEE Int. Conf. Data Min. ICDM - "Conference code: 88561" - "Export Date: 2 April 2012" - "Art. No.: 6137497" - "Sponsors: National Science Foundation (NSF) - Where Discoveries Begin; University of Technology Sydney; Google; Alberta Ingenuity Centre for Machine Learning; IBM Research" - 11 December 2011 through 11 December 2011 - "Source: Scopus" |
