Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/63426
Title: Most relevant spectral bands identification for brain cancer detection using hyperspectral imaging
Authors: Martinez, Beatriz
Leon, Raquel
Fabelo Gómez, Himar Antonio 
Ortega, Samuel 
Piñeiro, Juan F.
Zbigniew Szolna,Adam 
Hernandez, Maria
Espino, Carlos
O’shanahan, Aruma J.
Carrera, David
Bisshopp, Sara
Sosa, Coralia
Marquez, Mariano
Camacho, Rafael
de la Luz Plaza, Maria
Morera, Jesus
Marrero Callicó, Gustavo Iván 
UNESCO Clasification: 3314 Tecnología médica
Keywords: Brain cancer
Hyperspectral imaging
Intraoperative imaging
Feature selection
Image-guided surgery, et al
Issue Date: 2019
Project: Identificación Hiperespectral de Tumores Cerebrales (Ithaca)
Plataforma H2/Sw Distribuida Para El Procesamiento Inteligente de Información Sensorial Heterogenea en Aplicaciones de Supervisión de Grandes Espacios Naturales
Hyperspectral Imaging Cancer Detection (HELICoiD)
Journal: Sensors 
Abstract: Hyperspectral imaging (HSI) is a non-ionizing and non-contact imaging technique capable of obtaining more information than conventional RGB (red green blue) imaging. In the medical field, HSI has commonly been investigated due to its great potential for diagnostic and surgical guidance purposes. However, the large amount of information provided by HSI normally contains redundant or non-relevant information, and it is extremely important to identify the most relevant wavelengths for a certain application in order to improve the accuracy of the predictions and reduce the execution time of the classification algorithm. Additionally, some wavelengths can contain noise and removing such bands can improve the classification stage. The work presented in this paper aims to identify such relevant spectral ranges in the visual-and-near-infrared (VNIR) region for an accurate detection of brain cancer using in vivo hyperspectral images. A methodology based on optimization algorithms has been proposed for this task, identifying the relevant wavelengths to achieve the best accuracy in the classification results obtained by a supervised classifier (support vector machines), and employing the lowest possible number of spectral bands. The results demonstrate that the proposed methodology based on the genetic algorithm optimization slightly improves the accuracy of the tumor identification in ~5%, using only 48 bands, with respect to the reference results obtained with 128 bands, offering the possibility of developing customized acquisition sensors that could provide real-time HS imaging. The most relevant spectral ranges found comprise between 440.5–465.96 nm, 498.71–509.62 nm, 556.91–575.1 nm, 593.29–615.12 nm, 636.94–666.05 nm, 698.79–731.53 nm and 884.32–902.51 nm.
URI: http://hdl.handle.net/10553/63426
ISSN: 1424-8220
DOI: 10.3390/s19245481
Source: Sensors [1424-8220], v. 19 (24), artículo 5481
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