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Authors: Yuqian Li 1 ; Diana M. Sima 2 ; Sofie Van Cauter 3 ; Uwe Himmelreich 4 ; Yiming Pi 5 and Sabine Van Huffel 2

Affiliations: 1 University of Electronic Science and Technology of China, Katholieke Universiteit Leuven and IBBT-K.U.Leuven, China ; 2 Katholieke Universiteit Leuven and IBBT-K.U.Leuven, Belgium ; 3 University Hospitals of Leuven and Katholieke Universiteit Leuven, Belgium ; 4 Katholieke Universiteit Leuven, Belgium ; 5 University of Electronic Science and Technology of China, China

ISBN: 978-989-8425-89-8

Keyword(s): Non-negative Matrix Factorization (NMF), Blind Source Separation (BSS), Magnetic Resonance Spectroscopic Imaging (MRSI), Brain Glioma, Glioblastoma Multiforme (GBM).

Related Ontology Subjects/Areas/Topics: Applications and Services ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Medical Image Detection, Acquisition, Analysis and Processing

Abstract: The purpose of this paper is to introduce a hierarchical Non-negative Matrix Factorization (NMF) approach, customized for the problem of blindly separating brain glioma tumor tissue types using short-echo time proton magnetic resonance spectroscopic imaging (1H MRSI) signals. The proposed algorithm consists of two levels of NMF, where two constituent spectra are computed in each level. The first level is able to correctly detect the spectral profile corresponding to the most predominant tissue type, i.e., normal tissue, while the second level is optimized in order to detect two ‘abnormal’ spectral profiles so that the 3 recovered spectral profiles are least correlated with each other. The two-level decomposition is followed by the reestimation of the overall spatial distribution of each tissue type via standard Non-negative Least Square (NNLS). This method is demonstrated on in vivo short-TE 1H MRSI brain data of a glioblastoma multiforme patient and a grade II-III glioma patient. The results show the possibility of differentiating normal tissue, tumor tissue and necrotic tissue in the form of recovered tissue-specific spectra with accurate spatial distributions. (More)

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Paper citation in several formats:
Li, Y.; Sima, D.; Van Cauter, S.; Himmelreich, U.; Pi, Y. and Van Huffel, S. (2012). TISSUE TYPE DIFFERENTIATION FOR BRAIN GLIOMA USING NON-NEGATIVE MATRIX FACTORIZATION.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012) ISBN 978-989-8425-89-8, pages 25-31. DOI: 10.5220/0003734600250031

@conference{biosignals12,
author={Yuqian Li. and Diana M. Sima. and Sofie Van Cauter. and Uwe Himmelreich. and Yiming Pi. and Sabine Van Huffel.},
title={TISSUE TYPE DIFFERENTIATION FOR BRAIN GLIOMA USING NON-NEGATIVE MATRIX FACTORIZATION},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)},
year={2012},
pages={25-31},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003734600250031},
isbn={978-989-8425-89-8},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)
TI - TISSUE TYPE DIFFERENTIATION FOR BRAIN GLIOMA USING NON-NEGATIVE MATRIX FACTORIZATION
SN - 978-989-8425-89-8
AU - Li, Y.
AU - Sima, D.
AU - Van Cauter, S.
AU - Himmelreich, U.
AU - Pi, Y.
AU - Van Huffel, S.
PY - 2012
SP - 25
EP - 31
DO - 10.5220/0003734600250031

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