Journal
IEEE ACCESS
Volume 8, Issue -, Pages 8485-8501Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2963939
Keywords
Hyperspectral imaging; high performance computing; graphic processing unit; parallel processing; parallel architectures; image processing; biomedical engineering; medical diagnostic imaging; cancer detection; supervised classification; support vector machines; K-Nearest neighbors; principal component analysis; K-means; majority voting
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Funding
- 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
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Several causes make brain cancer identification a challenging task for neurosurgeons during the surgical procedure. The surgeons & x2019; naked eye sometimes is not enough to accurately delineate the brain tumor location and extension due to its diffuse nature that infiltrates in the surrounding healthy tissue. For this reason, a support system that provides accurate cancer delimitation is essential in order to improve the surgery outcomes and hence the patient & x2019;s quality of life. The brain cancer detection system developed as part of the & x201C;HypErspectraL Imaging Cancer Detection & x201D; (HELICoiD) European project meets this requirement exploiting a non-invasive technique suitable for medical diagnosis: the hyperspectral imaging (HSI). A crucial constraint that this system has to satisfy is providing a real-time response in order to not prolong the surgery. The large amount of data that characterizes the hyperspectral images, and the complex elaborations performed by the classification system make the High Performance Computing (HPC) systems essential to provide real-time processing. The most efficient implementation developed in this work, which exploits the Graphic Processing Unit (GPU) technology, is able to classify the biggest image of the database (worst case) in less than three seconds, largely satisfying the real-time constraint set to 1 minute for surgical procedures, becoming a potential solution to implement hyperspectral video processing in the near future.
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