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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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