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Multi-mode Microscopic Hyperspectral Imager for the Sensing of Biological Samples
Open AccessFeature PaperArticle

Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing

1
Faculty of Sciences, Universidad Autonoma de San Luis Potosi, San Luis Potosi 78295, Mexico
2
Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria, E35017 Las Palmas de Gran Canaria, Spain
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(16), 5686; https://doi.org/10.3390/app10165686
Received: 14 July 2020 / Revised: 2 August 2020 / Accepted: 12 August 2020 / Published: 17 August 2020
(This article belongs to the Special Issue Hyperspectral Imaging, Methods and Applications)
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. View Full-Text
Keywords: hyperspectral imaging; intraoperative imaging; brain cancer; linear unmixing; support vector machine hyperspectral imaging; intraoperative imaging; brain cancer; linear unmixing; support vector machine
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MDPI and ACS Style

Cruz-Guerrero, I.A.; Leon, R.; Campos-Delgado, D.U.; Ortega, S.; Fabelo, H.; Callico, G.M. Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing. Appl. Sci. 2020, 10, 5686.

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