Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/113934
Título: Hyperspectral VNIR and NIR Sensors for the Analysis of Human Normal Brain and Tumor Tissue
Autores/as: León, Raquel 
Fabelo, Himar 
Ortega Sarmiento, Samuel 
Callicó, Gustavo M. 
Clasificación UNESCO: 3314 Tecnología médica
Palabras clave: Brain Tumors
Hyperspectral Imaging
Image Registration
Statistical Analysis
Fecha de publicación: 2021
Proyectos: Watching the risk factors: Artificial intelligence and the prevention of chronic conditions 
Talent Imágenes Hiperespectrales Para Aplicaciones de Inteligencia Artificial 
Publicación seriada: Proceedings (Conference on Design of Circuits and Integrated Systems) 
Conferencia: 36th Conference on Design of Circuits and Integrated Systems - DCIS 2021
Resumen: Hyperspectral (HS) imaging (HSI) is arising as a novel imaging technique to delineate brain tumor tissue in surgical-Time. The accurate identification of the boundaries between tumor and normal tissue determines prolonged survival. In this work, a preliminary spectral analysis of different brain tissue was performed to identify features between different classes. A HSI dataset of in-vivo brain tissue was acquired by two HS cameras, covering the VNIR (Visual and Near-Infrared) [400-1000 nm] and NIR (Near-Infrared) [900-1700 nm] spectral ranges. Both HS images were registered using feature-based techniques with different geometric transformations to perform a spectral analysis of a certain pixel. Reflectance and absorbance spectral signatures were analyzed identifying spectral absorbance peaks related with hemoglobin and water. Finally, a statistical analysis was performed, where normal tissue (NT), tumor tissue (TT), and hypervascularized tissue (HT) were compared, obtaining highly statistically significance between HT-NT and HT-TT in both VNIR and NIR spectral ranges analyzed, and some no statistically significant differences between TT and NT in certain spectral ranges.
URI: http://hdl.handle.net/10553/113934
ISBN: 978-1-6654-2116-4
ISSN: 2640-5563
DOI: 10.1109/DCIS53048.2021.9666168
Fuente: 36th Conference on Design of Circuits and Integrated Systems, DCIS 2021[EISSN 2640-5563], (Enero 2021)
Colección:Actas de congresos
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