Unsupervised clustering of hyperspectral images of brain tissues by hierarchical non-negative matrix factorization

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 a 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 provides a preliminary study of the classification and sources of errors in the classification process.
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BIOIMAGING 2016 , Feb 2016, Rome, Italy. SCITEPRESS, 2 (77-84), pp.8, 2016, Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies,. <http://www.bioimaging.biostec.org/Home.aspx>
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Contributeur : Bangalore Ravi Kiran <>
Soumis le : lundi 29 février 2016 - 15:49:04
Dernière modification le : mardi 12 septembre 2017 - 11:41:17
Document(s) archivé(s) le : lundi 30 mai 2016 - 15:46:46

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Bangalore Ravi Kiran, Bogdan Stanciulescu, Jesus Angulo. Unsupervised clustering of hyperspectral images of brain tissues by hierarchical non-negative matrix factorization. BIOIMAGING 2016 , Feb 2016, Rome, Italy. SCITEPRESS, 2 (77-84), pp.8, 2016, Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies,. <http://www.bioimaging.biostec.org/Home.aspx>. <hal-01280453>

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