Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/44983
Title: A novel use of hyperspectral images for human brain cancer detection using in-vivo samples
Authors: Fabelo, Himar 
Ortega, Samuel 
Guerra, Raúl 
Callicó, Gustavo 
Szolna, Adam
Piñeiro, Juan F.
Tejedor, Miguel
López, Sebastián 
Sarmiento, Roberto 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Imaging techniques
Spectroscopy
Compressive spectral
Issue Date: 2016
Journal: BIOSIGNALS 2016 - 9th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016
Conference: 9th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2016 - Part of 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016 
Abstract: Hyperspectral Imaging is an emerging technology for medical diagnosis issues due to the fact that it is a noncontact, 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. Copyright © 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
URI: http://hdl.handle.net/10553/44983
ISBN: 9789897581700
Source: BIOSIGNALS 2016 - 9th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016, p. 311-320
Appears in Collections:Actas de congresos
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