6533b857fe1ef96bd12b458c
RESEARCH PRODUCT
Non-negative blind source separation techniques for tumor tissue typing using HR-MAS signals.
A. Croitor SavaS. Van HuffelM.c Martinez-bisbalDiana M. SimaBernardo Celdasubject
Magnetic Resonance SpectroscopyComputer scienceFeature extractionBlind signal separationSensitivity and SpecificitySpectral linePattern Recognition AutomatedNuclear magnetic resonanceDimension (vector space)medicineSource separationMagic angle spinningBiomarkers TumorHumansTypingDiagnosis Computer-Assistedmedicine.diagnostic_testArtificial neural networkbusiness.industryBrain NeoplasmsReproducibility of ResultsMagnetic resonance imagingPattern recognitionmedicine.diseaseTumor tissueArtificial intelligencebusinessGlioblastomaAlgorithmsGlioblastomadescription
Given High Resolution Magic Angle Spinning (HR-MAS) signals from several glioblastoma tumor subjects, the goal is to differentiate between tumor tissue types by separating the different sources that contribute to the profile of each spectrum. Blind source separation techniques are applied for obtaining characteristic profiles for necrosis, high cellular tumor and border tumor tissue, and providing the contribution (abundance) of each tumor tissue to the profile of the spectra. The problem is formulated as a non-negative source separation problem. We illustrate the effectiveness of the proposed methods and we analyze to which extent the dimension of the input space could influence the performance by comparing the results on the full magnitude signals and on dimensionally reduced spaces.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2010-11-25 |