Quantitative results of the supervised classification performed with the SVM classifier applied to the labeled data of each patient.

<p>(A) Overall accuracy results of supervised classification per SVM kernel type and patient. (B) and (C) Specificity and sensitivity results obtained using the SVM classifier with linear kernel for each patient and class employing the One-vs-All evaluation method.</p

Results of each step of the optimized spatial-spectral supervised classification of the five different patients.

<p>(A), (B), (C), (D) and (E) Synthetic RGB images generated from the HS cubes. (F), (G), (H), (I) and (J) Golden standard maps used for the supervised classification training. (K), (L), (M), (N) and (O) Supervised classification maps generated using the SVM algorithm. (P), (Q), (R), (S) and (T) FR-t-SNE one band representation of the HS cubes. (U), (V), (X), (Y) and (Z) Spatially optimized classification maps obtained after the KNN filtering.</p

Intra-operative hyperspectral acquisition system used during a neurosurgical procedure at the University Hospital Doctor Negrin of Las Palmas de Gran Canaria.

<p>Intra-operative hyperspectral acquisition system used during a neurosurgical procedure at the University Hospital Doctor Negrin of Las Palmas de Gran Canaria.</p

Results of each step of the proposed cancer detection algorithm applied to the five different patients.

<p>(A), (B), (C), (D) and (E) Segmentation maps generated using the HKM algorithm. (F), (G), (H), (I) and (J) MV classification maps. (K), (L), (M), (N) and (O) OMD maps that take into account only the major probability per class obtained from the MV algorithm. (P), (Q), (R), (S) and (T) TMD maps that take into account the first three major probabilities per class obtained from the MV algorithm.</p

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations - Fig 7

<p>Mean and variances of the pre-processed spectral signatures of the tumor, normal and blood vessel classes of the labeled pixels from patient 1 (A) and patient 2 (B), represented in red, black and blue color respectively.</p

Spectral signature of a grade IV glioblastoma tumor tissue.

<p>(A) Raw spectral signature. (B) Calibrated spectral signature. (C) HySIME filtered spectral signature. (D) Final pre-processed spectral signature.</p

KNN filtered maps obtained with different <i>K</i> and <i>λ</i> values.

<p>(A), (B), (C), (D) and (E) filtered maps obtained with <i>K</i> equal to 5, 10, 20, 40, and 60, while keeping <i>λ</i> value fixed to 1. (F), (G), (H), (I) and (J) filtered maps obtained with <i>λ</i> equal to 0, 1, 5, 10, and 100, while keeping <i>K</i> value fixed to 40.</p

Gold standard dataset for the supervised training process.

<p>Gold standard dataset for the supervised training process.</p

Brain cancer detection and delimitation algorithm overview diagram.

<p>(A) HS cube of in-vivo brain surface. (B) Pre-processing stage of the algorithm. (C) Database of labeling samples generation. (D) SVM model training process employing the labeled samples dataset. (E), (F) and (G) Algorithms that conform the spatial-spectral supervised classification stage. (H) and (I) Algorithms that generate the unsupervised segmentation map and the final HELICoiD TMD map, respectively.</p

Hybrid classification example based on a majority voting technique.

<p>The unsupervised segmentation map and the supervised classification maps are merged using the majority voting method.</p