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Descripción
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| 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. | |
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Internacional
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Si |
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Nombre congreso
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2016 IEEE Conference on Design and Architectures for Signal and Image Processing (DASIP) |
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Tipo de participación
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960 |
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Lugar del congreso
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Rennes, France |
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Revisores
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Si |
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ISBN o ISSN
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979-1-0922-7915-3 |
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DOI
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10.1109/DASIP.2016.7853831 |
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Fecha inicio congreso
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12/10/2016 |
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Fecha fin congreso
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14/10/2016 |
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Desde la página
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237 |
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Hasta la página
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238 |
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Título de las actas
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2016 Conference on Design and Architectures for Signal and Image Processing (DASIP) |