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Title: HELICoiD project: A new use of hyperspectral imaging for brain cancer detection in real-time during neurosurgical operations
Authors: Fabelo, Himar 
Ortega, Samuel 
Kabwama, Silvester
Callico, Gustavo M. 
Bulters, Diederik
Szolna, Adam
Pineiro, Juan F.
Sarmiento, Roberto 
UNESCO Clasification: 3314 Tecnología médica
Keywords: Imaging techniques
Compressive spectral
Issue Date: 2016
Journal: Proceedings of SPIE - The International Society for Optical Engineering 
Conference: Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2016
Abstract: Hyperspectral images allow obtaining large amounts of information about the surface of the scene that is captured by the sensor. Using this information and a set of complex classification algorithms is possible to determine which material or substance is located in each pixel. The HELICoiD (HypErspectraL Imaging Cancer Detection) project is a European FET project that has the goal to develop a demonstrator capable to discriminate, with high precision, between normal and tumour tissues, operating in real-time, during neurosurgical operations. This demonstrator could help the neurosurgeons in the process of brain tumour resection, avoiding the excessive extraction of normal tissue and unintentionally leaving small remnants of tumour. Such precise delimitation of the tumour boundaries will improve the results of the surgery. The HELICoiD demonstrator is composed of two hyperspectral cameras obtained from Headwall. The first one in the spectral range from 400 to 1000 nm (visible and near infrared) and the second one in the spectral range from 900 to 1700 nm (near infrared). The demonstrator also includes an illumination system that covers the spectral range from 400 nm to 2200 nm. A data processing unit is in charge of managing all the parts of the demonstrator, and a high performance platform aims to accelerate the hyperspectral image classification process. Each one of these elements is installed in a customized structure specially designed for surgical environments. Preliminary results of the classification algorithms offer high accuracy (over 95%) in the discrimination between normal and tumour tissues.
ISBN: 9781510601017
ISSN: 0277-786X
DOI: 10.1117/12.2223075
Source: Proceedings of SPIE - The International Society for Optical Engineering[ISSN 0277-786X],v. 9860 (986002)
Appears in Collections:Actas de congresos
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