Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/58282
Title: Parallel Implementations Assessment of a Spatial-Spectral Classifier for Hyperspectral Clinical Applications
Authors: Lazcano, Raquel
Madronal, Daniel
Florimbi, Giordana
Sancho, Jaime
Sanchez, Sergio
León Martín, Raquel
Fabelo Gómez, Himar Antonio 
Ortega Sarmiento, Samuel 
Torti, Emanuele
Salvador, Ruben
Marrero Martín, Margarita Luisa 
Leporati, Francesco
Juarez, Eduardo
Marrero Callicó, Gustavo Iván 
Sanz, Cesar
UNESCO Clasification: 3314 Tecnología médica
Keywords: Hyperspectral imaging
High performance computing
Parallel processing
Parallel architectures
Image processing, et al
Issue Date: 2019
Project: Identificación Hiperespectral de Tumores Cerebrales (Ithaca)
Plataforma H2/Sw Distribuida Para El Procesamiento Inteligente de Información Sensorial Heterogenea en Aplicaciones de Supervisión de Grandes Espacios Naturales 
Journal: IEEE Access 
Abstract: Hyperspectral (HS) imaging presents itself as a non-contact, non-ionizing and non-invasive technique, proven to be suitable for medical diagnosis. However, the volume of information contained in these images makes difficult providing the surgeon with information about the boundaries in real-time. To that end, High-Performance-Computing (HPC) platforms become necessary. This paper presents a comparison between the performances provided by five different HPC platforms while processing a spatial-spectral approach to classify HS images, assessing their main benefits and drawbacks. To provide a complete study, two different medical applications, with two different requirements, have been analyzed. The first application consists of HS images taken from neurosurgical operations; the second one presents HS images taken from dermatological interventions. While the main constraint for neurosurgical applications is the processing time, in other environments, as the dermatological one, other requirements can be considered. In that sense, energy efficiency is becoming a major challenge, since this kind of applications are usually developed as hand-held devices, thus depending on the battery capacity. These requirements have been considered to choose the target platforms: on the one hand, three of the most powerful Graphic Processing Units (GPUs) available in the market; and, on the other hand, a low-power GPU and a manycore architecture, both specifically thought for being used in battery-dependent environments.
URI: http://hdl.handle.net/10553/58282
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2938708
Source: IEEE Access [ISSN 2169-3536], v. 7, p. 152316-152333
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