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Authors: Bangalore Ravi Kiran ; Bogdan Stanciulescu and Jesus Angulo

Affiliation: PSL-Research University, France

Keyword(s): Hyperspectral Image, Unsupervised Clustering, Brain Tissue, Non-negative Matrix Factorization.

Related Ontology Subjects/Areas/Topics: Bioimaging ; Biomedical Engineering ; Feature Recognition and Extraction Methods ; Medical Imaging and Diagnosis

Abstract: Hyperspectral images of high spatial and spectral resolutions are employed to perform the challenging task of brain tissue characterization and subsequent segmentation for visualization of in-vivo images. Each pixel is high-dimensional spectrum. Working on the hypothesis of pure-pixels on account of high spectral resolution, we perform unsupervised clustering by hierarchical non-negative matrix factorization to identify the pure-pixel spectral signatures of blood, brain tissues, tumor and other materials. This subspace clustering was further used to train a random forest for subsequent classification of test set images constituent of in-vivo and ex-vivo images. Unsupervised hierarchical clustering helps visualize tissue structure in in-vivo test images and provides a inter-operative tool for surgeons. Furthermore the study also provide a preliminary study of the classification and sources of errors in the classification process.

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Paper citation in several formats:
Kiran, B.; Stanciulescu, B. and Angulo, J. (2016). Unsupervised Clustering of Hyperspectral Images of Brain Tissues by Hierarchical Non-negative Matrix Factorization. In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - BIOIMAGING; ISBN 978-989-758-170-0; ISSN 2184-4305, SciTePress, pages 77-84. DOI: 10.5220/0005697600770084

@conference{bioimaging16,
author={Bangalore Ravi Kiran. and Bogdan Stanciulescu. and Jesus Angulo.},
title={Unsupervised Clustering of Hyperspectral Images of Brain Tissues by Hierarchical Non-negative Matrix Factorization},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - BIOIMAGING},
year={2016},
pages={77-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005697600770084},
isbn={978-989-758-170-0},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - BIOIMAGING
TI - Unsupervised Clustering of Hyperspectral Images of Brain Tissues by Hierarchical Non-negative Matrix Factorization
SN - 978-989-758-170-0
IS - 2184-4305
AU - Kiran, B.
AU - Stanciulescu, B.
AU - Angulo, J.
PY - 2016
SP - 77
EP - 84
DO - 10.5220/0005697600770084
PB - SciTePress

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