Parallel Implementations Assessment of a Spatial-Spectral Classifier for
  Hyperspectral Clinical Applications
Raquel Lazcano Daniel Madroñal Giordana Florimbi Jaime Sancho Sergio Sanchez Raquel Leon Himar Fabelo Samuel Ortega Emanuele Torti Ruben Salvador Margarita Marrero-Martin Francesco Leporati Eduardo Juarez Gustavo M Callico Cesar Sanz
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.