A Novel Use of Hyperspectral Images for Human Brain Cancer Detection using in-Vivo Samples

Hyperspectral Imaging is an emerging technology for medical diagnosis issues due to the fact that it is a non-contact, non-ionizing and non-invasive sensing technique. The work presented in this paper tries to establish a novel way in the use of hyperspectral images to help neurosurgeons to accurately determine the tumour boundaries in the process of brain tumour resection, avoiding excessive extraction of healthy tissue and the accidental leaving of un-resected small tumour tissues. So as to do that, a hyperspectral database of in-vivo human brain samples has been created and a procedure to label the pixels diagnosed by the pathologists has been described. A total of 24646 samples from normal and tumour tissues from 13 different patients have been obtained. A pre-processing chain to homogenize the spectral signatures has been developed, obtaining 3 types of datasets (using different pre-processing chain) in order to determine which one provides the best classification results using a Random Forest classifier. The experimental results of this supervised classification algorithm to distinguish between normal and tumour tissues have achieved more than 99% of accuracy.