Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/106772
Title: Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing
Authors: Cruz-Guerrero, Ines A.
Leon, Raquel
Campos-Delgado, Daniel U.
Ortega Sarmiento, Samuel 
Fabelo Gómez, Himar Antonio 
Marrero Callicó, Gustavo Iván 
UNESCO Clasification: 3314 Tecnología médica
Keywords: Hyperspectral imaging
Intraoperative imaging
Brain cancer
Linear unmixing
Support vector machine
Issue Date: 2020
Journal: Applied Sciences 
Abstract: Hyperspectral imaging is a multidimensional optical technique with the potential of providing fast and accurate tissue classification. The main challenge is the adequate processing of the multidimensional information usually linked to long processing times and significant computational costs, which require expensive hardware. In this study, we address the problem of tissue classification for intraoperative hyperspectral images of in vivo brain tissue. For this goal, two methodologies are introduced that rely on a blind linear unmixing (BLU) scheme for practical tissue classification. Both methodologies identify the characteristic end-members related to the studied tissue classes by BLU from a training dataset and classify the pixels by a minimum distance approach. The proposed methodologies are compared with a machine learning method based on a supervised support vector machine (SVM) classifier. The methodologies based on BLU achieve speedup factors of ~459 and ~429 compared to the SVM scheme, while keeping constant and even slightly improving the classification performance
URI: http://hdl.handle.net/10553/106772
ISSN: 2076-3417
DOI: 10.3390/app10165686
Source: Applied Sciences [ISSN 2076-3417], n. 10(16), 5686
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