VNIR–NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection

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...

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Autores principales:	Raquel Leon, Himar Fabelo, Samuel Ortega, Juan F. Piñeiro, Adam Szolna, Maria Hernandez, Carlos Espino, Aruma J. O’Shanahan, David Carrera, Sara Bisshopp, Coralia Sosa, Mariano Marquez, Jesus Morera, Bernardino Clavo, Gustavo M. Callico
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d19c28d9d72448d1967396cf141a0410
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spelling	oai:doaj.org-article:d19c28d9d72448d1967396cf141a04102021-12-02T18:37:09ZVNIR–NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection10.1038/s41598-021-99220-02045-2322https://doaj.org/article/d19c28d9d72448d1967396cf141a04102021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99220-0https://doaj.org/toc/2045-2322Abstract 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.Raquel LeonHimar FabeloSamuel OrtegaJuan F. PiñeiroAdam SzolnaMaria HernandezCarlos EspinoAruma J. O’ShanahanDavid CarreraSara BisshoppCoralia SosaMariano MarquezJesus MoreraBernardino ClavoGustavo M. CallicoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Raquel Leon Himar Fabelo Samuel Ortega Juan F. Piñeiro Adam Szolna Maria Hernandez Carlos Espino Aruma J. O’Shanahan David Carrera Sara Bisshopp Coralia Sosa Mariano Marquez Jesus Morera Bernardino Clavo Gustavo M. Callico VNIR–NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection
description	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.
format	article
author	Raquel Leon Himar Fabelo Samuel Ortega Juan F. Piñeiro Adam Szolna Maria Hernandez Carlos Espino Aruma J. O’Shanahan David Carrera Sara Bisshopp Coralia Sosa Mariano Marquez Jesus Morera Bernardino Clavo Gustavo M. Callico
author_facet	Raquel Leon Himar Fabelo Samuel Ortega Juan F. Piñeiro Adam Szolna Maria Hernandez Carlos Espino Aruma J. O’Shanahan David Carrera Sara Bisshopp Coralia Sosa Mariano Marquez Jesus Morera Bernardino Clavo Gustavo M. Callico
author_sort	Raquel Leon
title	VNIR–NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection
title_short	VNIR–NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection
title_full	VNIR–NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection
title_fullStr	VNIR–NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection
title_full_unstemmed	VNIR–NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection
title_sort	vnir–nir hyperspectral imaging fusion targeting intraoperative brain cancer detection
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d19c28d9d72448d1967396cf141a0410
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VNIR–NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection

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