Unsupervised clustering of hyperspectral images of brain tissues by hierarchical non-negative matrix factorizationReport as inadecuate




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1 CAOR - Centre de Robotique 2 CMM - Centre de Morphologie Mathématique

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.

Keywords : Hyperspectral Image Hierarchical clustering Brain tissue Non-negative matrix factorization





Author: Bangalore Ravi Kiran - Bogdan Stanciulescu - Jesus Angulo -

Source: https://hal.archives-ouvertes.fr/



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