Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICDMW.2011.41 | ICDM Workshops |
Keywords | Field | DocType |
data mining,brain tumor pathological area,convex-nonnegative matrix factorization technique,pathological area,non-negative matrix factorization,brain tumor,pattern recognition,mrs signal source,tissue type-specific,magnetic resonance,brain volume,pathological area delimitation,non negative matrix factorization,image recognition,neurophysiology,nonnegative matrix factorization,cancer,matrix decomposition,magnetic resonance spectroscopy,complex data | Data mining,Neurophysiology,Computer science,Matrix decomposition,Pathological,Brain tumor,Non-negative matrix factorization,Magnetic resonance imaging | Conference |
ISBN | Citations | PageRank |
978-1-4673-0005-6 | 0 | 0.34 |
References | Authors | |
4 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sandra Ortega-Martorell | 1 | 33 | 7.01 |
| Paulo J. G. Lisboa | 2 | 372 | 40.48 |
| Alfredo Vellido | 3 | 373 | 51.92 |
| Rui V. Simões | 4 | 0 | 0.34 |
| Margarida Julia-Sape | 5 | 8 | 1.57 |
| Carles Arus | 6 | 43 | 3.79 |
| Ortega-Martorell, S. | 7 | 0 | 0.34 |
| Simoes, R.V. | 8 | 0 | 0.34 |