Journal
ELECTRONICS
Volume 7, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/electronics7110283
Keywords
Graphics Processing Units (GPUs); CUDA; OpenMP; OpenCL; K-means; brain cancer detection; hyperspectral imaging; unsupervised clustering
Categories
Funding
- Canary Islands Government through the ACIISI (Canarian Agency for Research, Innovation and the Information Society)
- ITHACA project Hyperspectral Identification of Brain Tumors [ProID2017010164]
- Spanish Government
- European Union (FEDER funds) [TEC2017-86722-C4-1-R]
- Agencia Canaria de Investigacion, Innovacion y Sociedad de la Informacion (ACIISI) of the Conserjeria de Economia, Industria, Comercio y Conocimiento of the Gobierno de Canarias - European Social Fund (FSE) (POC 2014-2020, Eje 3 Tema Prioritario 74(85
- 2016 PhD Training Program for Research Staff of the University of Las Palmas de Gran Canaria
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The precise delineation of brain cancer is a crucial task during surgery. There are several techniques employed during surgical procedures to guide neurosurgeons in the tumor resection. However, hyperspectral imaging (HSI) is a promising non-invasive and non-ionizing imaging technique that could improve and complement the currently used methods. The HypErspectraL Imaging Cancer Detection (HELICoiD) European project has addressed the development of a methodology for tumor tissue detection and delineation exploiting HSI techniques. In this approach, the K-means algorithm emerged in the delimitation of tumor borders, which is of crucial importance. The main drawback is the computational complexity of this algorithm. This paper describes the development of the K-means clustering algorithm on different parallel architectures, in order to provide real-time processing during surgical procedures. This algorithm will generate an unsupervised segmentation map that, combined with a supervised classification map, will offer guidance to the neurosurgeon during the tumor resection task. We present parallel K-means clustering based on OpenMP, CUDA and OpenCL paradigms. These algorithms have been validated through an in-vivo hyperspectral human brain image database. Experimental results show that the CUDA version can achieve a speed-up of with respect to a sequential processing. The remarkable result obtained in this paper makes possible the development of a real-time classification system.
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