Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/46814
Título: Demo: HELICoiD tool demonstrator for real-time brain cancer detection
Autores/as: Salvador, R.
Fabelo, H. 
Lazcano, R.
Ortega, S. 
Madroñal, D.
Callicó, G. M. 
Juárez, E.
Sanz, C.
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: Hyperspectral imaging
Tumors
Surgery
Cancer
Real-time systems
Fecha de publicación: 2017
Publicación seriada: Conference on Design and Architectures for Signal and Image Processing, DASIP
Conferencia: 2016 Conference on Design and Architectures for Signal and Image Processing, DASIP 2016 
Resumen: 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
Fuente: Conference on Design and Architectures for Signal and Image Processing, DASIP[ISSN 2164-9766] (7853831), p. 237-238
Colección:Actas de congresos
miniatura
Adobe PDF (382,81 kB)
Vista completa

Citas SCOPUSTM   

4
actualizado el 24-mar-2024

Citas de WEB OF SCIENCETM
Citations

3
actualizado el 25-feb-2024

Visitas

86
actualizado el 04-feb-2023

Descargas

143
actualizado el 04-feb-2023

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.