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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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https://hal.archives-ouvertes.fr/hal-01280453
Contributor : Bangalore Ravi Kiran <>
Submitted on : Monday, February 29, 2016 - 3:49:04 PM
Last modification on : Thursday, April 9, 2020 - 5:08:13 PM

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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. pp.8, ⟨10.5220/0005697600770084⟩. ⟨hal-01280453⟩

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