Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/58283
Title: In-Vivo Hyperspectral Human Brain Image Database for Brain Cancer Detection
Authors: Fabelo Gómez, Himar Antonio 
Ortega Sarmiento, Samuel 
Zbigniew Szolna,Adam 
Bulters, Diederik
Piñeiro, Juan F.
Kabwama, Silvester
J-O'Shanahan, Aruma
Bulstrode, Harry
Bisshopp, Sara
Kiran, B. Ravi
Ravi, Daniele
Lazcano, Raquel
Madroñal, Daniel
Sosa Pérez, Coralia de Las Nieve 
Espino, Carlos
Marquez, Mariano
Plaza Pérez, María de la Luz 
Camacho Galán,Rafael 
Carrera, David
Hernandez, Maria
Marrero Callicó, Gustavo Iván 
Morera Molina, Jesús Manuel 
Stanciulescu, Bogdan
Yang, Guang Zhong
Salvador Perea, Rubén
Juarez, Eduardo
Sanz, César
Sarmiento Rodríguez, Roberto 
UNESCO Clasification: 3314 Tecnología médica
Keywords: Hyperspectral imaging
Cancer detection
Biomedical imaging
Medical diagnostic imaging
Image databases
Issue Date: 2019
Journal: IEEE Access 
Abstract: The use of hyperspectral imaging for medical applications is becoming more common in recent years. One of the main obstacles that researchers find when developing hyperspectral algorithms for medical applications is the lack of specific, publicly available, and hyperspectral medical data. The work described in this paper was developed within the framework of the European project HELICoiD (HypErspectraL Imaging Cancer Detection), which had as a main goal the application of hyperspectral imaging to the delineation of brain tumors in real-time during neurosurgical operations. In this paper, the methodology followed to generate the first hyperspectral database of in-vivo human brain tissues is presented. Data was acquired employing a customized hyperspectral acquisition system capable of capturing information in the Visual and Near InfraRed (VNIR) range from 400 to 1000 nm. Repeatability was assessed for the cases where two images of the same scene were captured consecutively. The analysis reveals that the system works more efficiently in the spectral range between 450 and 900 nm. A total of 36 hyperspectral images from 22 different patients were obtained. From these data, more than 300 000 spectral signatures were labeled employing a semi-automatic methodology based on the spectral angle mapper algorithm. Four different classes were defined: normal tissue, tumor tissue, blood vessel, and background elements. All the hyperspectral data has been made available in a public repository.
URI: http://hdl.handle.net/10553/58283
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2904788
Source: IEEE Access [ISSN 2169-3536], v. 7, p. 39098 - 39116
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