Dataset: Parallel Implementations Assessment of a Spatial-Spectral Classifier for Hyperspectral Clinical Applications

Dataset: Parallel Implementations Assessment of a Spatial-Spectral Classifier for Hyperspectral Clinical Applications

Citation Author(s):
Himar
Fabelo
Universidad de Las Palmas de Gran Canaria
Samuel
Ortega
Universidad de Las Palmas de Gran Canaria
Raquel
León
Universidad de Las Palmas de Gran Canaria
Gustavo
Callico
Universidad de Las Palmas de Gran Canaria
Submitted by:
Raquel Lazcano
Last updated:
Thu, 08/29/2019 - 05:30
DOI:
10.21227/pn25-nj87
Data Format:
Links:
License:
Creative Commons Attribution
Dataset Views:
251
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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.

Instructions: 

Dataset description

 

1) Size of the images

 

- PD1C1: 1000 samples x 1000 lines x 100 bands

- PD1C2: 1000 samples x 1000 lines x 100 bands

- PD1C3: 1000 samples x 1000 lines x 100 bands

 

2) Image composition

 

- The information is stored band by band

- Within each band, the information is stored line by line

- The data type is float

 

3) Important information

 

This database only contains the dermatological images. The three brain images, obtained within the context of HELICoiD EU project, are already available in the following repository:

https://hsibraindatabase.iuma.ulpgc.es/

 

For downloading the brain images used in this research:

- PB1C1: Op12C1

- PB2C1: Op15C1

- PB3C1: Op20C1

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[1] , "Dataset: Parallel Implementations Assessment of a Spatial-Spectral Classifier for Hyperspectral Clinical Applications", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/pn25-nj87. Accessed: Sep. 20, 2019.
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title = {Dataset: Parallel Implementations Assessment of a Spatial-Spectral Classifier for Hyperspectral Clinical Applications},
year = {2019} }
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. (2019). Dataset: Parallel Implementations Assessment of a Spatial-Spectral Classifier for Hyperspectral Clinical Applications. IEEE Dataport. http://dx.doi.org/10.21227/pn25-nj87
, 2019. Dataset: Parallel Implementations Assessment of a Spatial-Spectral Classifier for Hyperspectral Clinical Applications. Available at: http://dx.doi.org/10.21227/pn25-nj87.
. (2019). "Dataset: Parallel Implementations Assessment of a Spatial-Spectral Classifier for Hyperspectral Clinical Applications." Web.
1. . Dataset: Parallel Implementations Assessment of a Spatial-Spectral Classifier for Hyperspectral Clinical Applications [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/pn25-nj87
. "Dataset: Parallel Implementations Assessment of a Spatial-Spectral Classifier for Hyperspectral Clinical Applications." doi: 10.21227/pn25-nj87