# Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing
## 5 Results and Discussion
### Metrics' Evaluation for the Distance to End-Members' Sets  
Method A is based on first detecting the rubber ring markers, used by the surgeon to identify tumor and healthy tissues, and then removing this information to estimate the characteristic end-members. The evaluation of this methodology was performed by using the six test HS images in Figure 1 (P008-01, P008-02, P012-01, P012-02, P015-01, and P020-01), and the different distances in Equations (12)-(16): Manhattan metric, Euclidean metric, correlation metric, Mahalanobis metric, and SAM.  
Figure 4 shows the average performance classification results obtained for each metric. Figure 4a shows the accuracy, sensitivity, and specificity results, where the accuracy value is similar in all metrics. Besides that, the sensitivity performance for the NT and BG classes with all metrics is also equivalent. Meanwhile, the main performance differences are observed in the TT and HT classes. The classification with the Manhattan metric achieves the best sensitivity in the HT class with a value of ~70%, while the correlation metric offers the worst performance (~47%). However, related to the TT class, the correlation metric achieves the best sensitivity results (50%), and the worst performance is obtained by the Manhattan metric (~32%). Finally, the specificity results are mostly consistent in all classes; for the NT class, the performance is always higher than 70%, and higher than 90% for the TT, HT, and BG classes. Figure 4b shows the MCC results, where we observe that all metrics offer similar performance, except the correlation metric in the HT class, which exhibits a reduction of ~7% related to the best solution. However, this distance achieves the best result in the TT class. For this reason, the correlation metric provides on average the best results, especially highlighted in the TT class, being highly relevant due to the nature of our clinical application.  
Figure 3: Block diagram of the stages corresponding to Method B.  
Figure 5 shows the classification maps obtained with each metric for Method A. Figure 5a illustrates the synthetic RGB images with the tumor area marked by a yellow line, and in Figure 5b-f are the results with all metrics in Equations (12)-(16).  
As highlighted by the accuracy performance in Figure 4a, the classification is quite consistent with all metrics. Nonetheless, we obtain a better definition of the tumor areas in the HS images by using the correlation metric, for example in images P012-02 and P015-01. In particular, for image P012-02 with the Manhattan metric (Figure 5b), some HT pixels in the tumor area are misclassified; however, by using the correlation metric (Figure 5d), the tumor area is correctly identified. Furthermore, the correlation metric in the same image shows an homogeneous tumor area with respect to other distances. Note that for the P020-01 image, none of the metrics are able to distinguish the TT class, since the synthetic RGB image highlights that the marked tumor area presents a similar colorization to the NT class, which is consistent with the result in [29]. As stated in such previous work, this misclassification of the TT class could be produced by the lack of a more complete database that takes into account the inter-patient spectra variability.  
Figure 4: Average classification performance of Method A by using all metrics in Equations (12)–(16). (**a**) Accuracy, sensitivity, and specificity results. (**b**) MCC (normalized).