Please use this identifier to cite or link to this item:
Title: VNIR–NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection
Authors: León, Raquel 
Fabelo, Himar 
Ortega, Samuel 
Piñeiro, Juan F.
Szolna, Adam 
Hernández, Maria
Espino, Carlos
O’Shanahan, Aruma J.
Carrera, David
Bisshopp, Sara 
Sosa, Coralia
Marquez, Mariano
Morera, Jesus
Clavo, Bernardino 
Callicó, Gustavo M. 
UNESCO Clasification: 32 Ciencias médicas
320711 Neuropatología
330790 Microelectrónica
Keywords: Biomedical engineering
Brain imaging
Cancer imaging
CNS cancer
Computational science, et al
Issue Date: 2021
Project: Identificación Hiperespectral de Tumores Cerebrales (Ithaca) 
Watching the risk factors: Artificial intelligence and the prevention of chronic conditions 
Journal: Scientific Reports 
Abstract: Currently, intraoperative guidance tools used for brain tumor resection assistance during surgery have several limitations. Hyperspectral (HS) imaging is arising as a novel imaging technique that could offer new capabilities to delineate brain tumor tissue in surgical-time. However, the HS acquisition systems have some limitations regarding spatial and spectral resolution depending on the spectral range to be captured. Image fusion techniques combine information from different sensors to obtain an HS cube with improved spatial and spectral resolution. This paper describes the contributions to HS image fusion using two push-broom HS cameras, covering the visual and near-infrared (VNIR) [400–1000 nm] and near-infrared (NIR) [900–1700 nm] spectral ranges, which are integrated into an intraoperative HS acquisition system developed to delineate brain tumor tissue during neurosurgical procedures. Both HS images were registered using intensity-based and feature-based techniques with different geometric transformations to perform the HS image fusion, obtaining an HS cube with wide spectral range [435–1638 nm]. Four HS datasets were captured to verify the image registration and the fusion process. Moreover, segmentation and classification methods were evaluated to compare the performance results between the use of the VNIR and NIR data, independently, with respect to the fused data. The results reveal that the proposed methodology for fusing VNIR–NIR data improves the classification results up to 21% of accuracy with respect to the use of each data modality independently, depending on the targeted classification problem.
ISSN: 2045-2322
DOI: 10.1038/s41598-021-99220-0
Source: Scientific Reports [EISSN 2045-2322], v. 11 (1), 19696, (Diciembre 2021)
Appears in Collections:Artículos
Adobe PDF (5,01 MB)
Show full item record


checked on Jun 4, 2023

Page view(s)

checked on May 14, 2023


checked on May 14, 2023

Google ScholarTM




Export metadata

Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.