Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing

Ines A. Cruz-Guerrero, Raquel Leon, Daniel U. Campos-Delgado, Samuel Ortega, Himar Fabelo, Gustavo M. Callico
2020 Applied Sciences  
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
more » ... ced 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.
doi:10.3390/app10165686 fatcat:5yjl5c5qnbejpcrxqcoo73aule