Unsupervised Clustering of Hyperspectral Images of Brain Tissues by Hierarchical Non-negative Matrix Factorization

Bangalore Ravi Kiran, Bogdan Stanciulescu, Jesus Angulo

2016

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.

Paper Citation

in Harvard Style

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 - Volume 2: BIOIMAGING, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 77-84. DOI: 10.5220/0005697600770084

in Bibtex Style

@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 - Volume 2: BIOIMAGING, (BIOSTEC 2016)},
year={2016},
pages={77-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005697600770084},
isbn={978-989-758-170-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2016)
TI - Unsupervised Clustering of Hyperspectral Images of Brain Tissues by Hierarchical Non-negative Matrix Factorization
SN - 978-989-758-170-0
AU - Kiran B.
AU - Stanciulescu B.
AU - Angulo J.
PY - 2016
SP - 77
EP - 84
DO - 10.5220/0005697600770084