Published 2021

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Publication details

Journal : Scientific Reports , vol. 11 , p. 12 , 2021

International Standard Numbers :
Printed : 2045-2322
Electronic : 2045-2322

Publication type : Academic article

Contributors : Leon, Raquel; Fabelo, Himar; Ortega Sarmiento, Samuel; Piñeiro, Juan F.; Szolna, Adam; Hernandez, Maria; Espino, Carlos; O'Shanahan, Aruma J.; Carrera, David; Bisshopp, Sara; Sosa, Coralia; Marquez, Mariano; Morera, Jesus; Clavo, Bernardino; Callico, Gustavo M.

Research areas

Quality and measurement methods

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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.


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