Nonlinear extended blind end-member and abundance extraction for hyperspectral images

Hyperspectral Imaging Benchmark based on Machine Learning for Intraoperative Brain Tumour Detection

“…We have developed an intraoperative customized HS acquisition system, employed in three data acquisition campaigns, to collect 62 HS images of exposed brain surface from 34 different patients that underwent surgery due to brain cancer or another disease that required surgery. Using this extended database with respect to previous works [30][31][32]39,40 , we have analysed the spectral characteristics of the brain tissue (normal and tumour) and blood vessels, and the different tumour /15 types according to their malignancy grades (G1 to G4) and origin (primary and secondary), performing a statistical analysis between all the medians of each spectral channel when comparing the different classes and tumour grades and origins. Moreover, a robust 5-fold cross-validation approach was used to evaluate eight different processing algorithms, first using only spectral information, and then using both spatial and spectral information following a processing framework that we previously developed 30 .…”

“…Supervised Classification Algorithms ML algorithms used in this work were based on SVM, RF, and KNN classifiers, while the DL algorithm employed was a twolayer one-dimensional DNN. Moreover, two unmixing-based algorithms were studied (EBEAE and NEBEAE) using their MATLAB implementations 40,44 . SVMs are widely used for classification and regression purposes 45 .…”

“…The EBEAE is employed in non-negative datasets using a linear mixing model to perform the estimation of characteristic spectral endmembers and their abundances 44 . The NEBEAE is a nonlinear version of EBEAE, capable of quantifying non-linear optical interactions during the acquisition process, which is also robust against noise 40 . In both cases, different hyperparameters can be modified to adjust the similarity among endmembers (ρ) and the entropy of the abundances (𝛾).…”

“…In both cases, different hyperparameters can be modified to adjust the similarity among endmembers (ρ) and the entropy of the abundances (𝛾). These algorithms have been previously used to identify glioblastoma tumour in pathological slides and in-vivo tissue using HS data 32,39,40,44,49 . In this case, the characteristic endmembers were estimated by the EBEAE and NEBEAE algorithms, respectively.…”