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Communication Dans Un Congrès Année : 2016

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

Bangalore Ravi Kiran
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Bogdan Stanciulescu
Jesus Angulo

Résumé

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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Dates et versions

hal-01280453 , version 1 (29-02-2016)

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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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