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Quantifying brain tumor tissue abundance in HR‐MAS spectra using non‐negative blind source separation techniques
Author(s) -
Croitor Sava Anca Ramona,
MartinezBisbal M. Carmen,
Sima Diana Maria,
Calvar Jorge,
Esteve Vicente,
Celda Bernardo,
Himmelreich Uwe,
Van Huffel Sabine
Publication year - 2012
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2456
Subject(s) - non negative matrix factorization , brain tissue , spectral line , blind signal separation , source separation , resolution (logic) , brain tumor , nuclear magnetic resonance , analytical chemistry (journal) , mathematics , computer science , biological system , materials science , chemistry , matrix decomposition , pathology , biomedical engineering , physics , algorithm , artificial intelligence , chromatography , biology , medicine , eigenvalues and eigenvectors , computer network , channel (broadcasting) , quantum mechanics , astronomy
Given high‐resolution magic angle spinning (HR‐MAS) spectra from several glial tumor subjects, our 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, highly cellular tumor and border tumor tissue and providing the contribution (abundance) of each of these tumor tissue types to the profile of each spectrum. The problem is formulated as a non‐negative source separation problem. Non‐negative matrix factorization, convex analysis of non‐negative sources and non‐negative independent component analysis methods are considered. The results are in agreement with the pathology obtained by the histopathological examination that succeeded the HR‐MAS measurements. Furthermore, an analysis to verify to which extent the dimension of the input space, the input features and the number of sources to be extracted from the HR‐MAS data could influence the performance of the source separation is presented. Copyright © 2012 John Wiley & Sons, Ltd.