title: Non-negative blind source separation techniques for tumor tissue typing using HR-MAS signals.
abstract: 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.
authors.affiliation: Department of Electrical Engineering (ESAT-SCD) - Biomed, Katholieke Universiteit Leuven, Belgium. anca.croitor@esat.kuleuven.be
authors.forename: A
authors.initials: A
authors.lastname: Croitor Sava
authors.forename: D M
authors.initials: DM
authors.lastname: Sima
authors.forename: M C
authors.initials: MC
authors.lastname: Martinez-Bisbal
authors.forename: B
authors.initials: B
authors.lastname: Celda
authors.forename: S
authors.initials: S
authors.lastname: Van Huffel
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005909:Glioblastoma / Q000175:diagnosis* / Q000378:metabolism*; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor