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Link to original content: https://doi.org/10.1007/978-3-030-25636-4_24
HaraliCU: GPU-Powered Haralick Feature Extraction on Medical Images Exploiting the Full Dynamics of Gray-Scale Levels | SpringerLink
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HaraliCU: GPU-Powered Haralick Feature Extraction on Medical Images Exploiting the Full Dynamics of Gray-Scale Levels

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Parallel Computing Technologies (PaCT 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11657))

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Abstract

Image texture extraction and analysis are fundamental steps in Computer Vision. In particular, considering the biomedical field, quantitative imaging methods are increasingly gaining importance since they convey scientifically and clinically relevant information for prediction, prognosis, and treatment response assessment. In this context, radiomic approaches are fostering large-scale studies that can have a significant impact in the clinical practice. In this work, we focus on Haralick features, the most common and clinically relevant descriptors. These features are based on the Gray-Level Co-occurrence Matrix (GLCM), whose computation is considerably intensive on images characterized by a high bit-depth (e.g., 16 bits), as in the case of medical images that convey detailed visual information. We propose here HaraliCU, an efficient strategy for the computation of the GLCM and the extraction of an exhaustive set of the Haralick features. HaraliCU was conceived to exploit the parallel computation capabilities of modern Graphics Processing Units (GPUs), allowing us to achieve up to \(\sim \!20\times \) speed-up with respect to the corresponding C++ coded sequential version. Our GPU-powered solution highlights the promising capabilities of GPUs in the clinical research.

L. Rundo and A. Tangherloni—Contributed equally.

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Notes

  1. 1.

    https://uk.mathworks.com/matlabcentral/fileexchange/22187-glcm-texture-features.

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Acknowledgment

This work was partially supported by The Mark Foundation for Cancer Research and Cancer Research UK Cambridge Centre [C9685/A25177]. Additional support has been provided by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

Author information

Authors and Affiliations

  1. Department of Radiology, University of Cambridge, Cambridge, UK

    Leonardo Rundo, Ramona Woitek & Evis Sala

  2. Cancer Research UK Cambridge Centre, Cambridge, UK

    Leonardo Rundo, Ramona Woitek & Evis Sala

  3. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Leonardo Rundo, Andrea Tangherloni, Simone Galimberti, Marco S. Nobile & Giancarlo Mauri

  4. Department of Haematology, University of Cambridge, Cambridge, UK

    Andrea Tangherloni

  5. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK

    Andrea Tangherloni

  6. Wellcome Trust – Medical Research Council Cambridge Stem Cell Institute, Cambridge, UK

    Andrea Tangherloni

  7. Department of Human and Social Sciences, University of Bergamo, Bergamo, Italy

    Paolo Cazzaniga

  8. SYSBIO.IT Centre of Systems Biology, Milano, Italy

    Paolo Cazzaniga, Marco S. Nobile & Giancarlo Mauri

  9. Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria

    Ramona Woitek

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  1. Leonardo Rundo
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  2. Andrea Tangherloni
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Correspondence to Giancarlo Mauri .

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  1. Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk State University, Novosibirsk State Technical University, Novosibirsk, Russia

    Victor Malyshkin

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Rundo, L. et al. (2019). HaraliCU: GPU-Powered Haralick Feature Extraction on Medical Images Exploiting the Full Dynamics of Gray-Scale Levels. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2019. Lecture Notes in Computer Science(), vol 11657. Springer, Cham. https://doi.org/10.1007/978-3-030-25636-4_24

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  • DOI: https://doi.org/10.1007/978-3-030-25636-4_24

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