Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/46814
Title: Demo: HELICoiD tool demonstrator for real-time brain cancer detection
Authors: Salvador, R.
Fabelo, H. 
Lazcano, R.
Ortega, S. 
Madroñal, D.
Callicó, G. M. 
Juárez, E.
Sanz, C.
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Hyperspectral imaging
Tumors
Surgery
Cancer
Real-time systems
Issue Date: 2017
Journal: Conference on Design and Architectures for Signal and Image Processing, DASIP
Conference: 2016 Conference on Design and Architectures for Signal and Image Processing, DASIP 2016 
Abstract: In this paper, a demonstrator of three different elements of the EU FET HELICoiD project is introduced. The goal of this demonstration is to show how the combination of hyperspectral imaging and machine learning can be a potential solution to precise real-time detection of tumor tissues during surgical operations. The HELICoiD setup consists of two hyperspectral cameras, a scanning unit, an illumination system, a data processing system and an EMB01 accelerator platform, which hosts an MPPA-256 manycore chip. All the components are mounted fulfilling restrictions from surgical environments, as shown in the accompanying video recorded at the operating room. An in-vivo human brain hyperspectral image data base, obtained at the University Hospital Doctor Negrin in Las Palmas de Gran Canaria, has been employed as input to different supervised classification algorithms (SVM, RF, NN) and to a spatial-spectral filtering stage (SVM-KNN). The resulting classification maps are shown in this demo. In addition, the implementation of the SVM-KNN classification algorithm on the MPPA EMB01 platform is demonstrated in the live demo.
URI: http://hdl.handle.net/10553/46814
ISBN: 9791092279153
ISSN: 2164-9766
DOI: 10.1109/DASIP.2016.7853831
Source: Conference on Design and Architectures for Signal and Image Processing, DASIP[ISSN 2164-9766] (7853831), p. 237-238
Appears in Collections:Actas de congresos
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