UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

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

Fabelo, H; Ortega, S; Ravi, D; Kiran, BR; Sosa, C; Bulters, D; Callicó, GM; ... Sarmiento, R; + view all (2018) Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations. PLoS ONE , 13 (3) , Article e0193721. 10.1371/journal.pone.0193721. Green open access

[img]
Preview
Text
journal.pone.0193721.pdf - ["content_typename_Published version" not defined]

Download (3MB) | Preview

Abstract

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a noncontact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

Type: Article
Title: Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0193721
Publisher version: http://doi.org/10.1371/journal.pone.0193721
Language: English
Additional information: Copyright: © 2018 Fabelo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
URI: http://discovery.ucl.ac.uk/id/eprint/10046554
Downloads since deposit
8Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item