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Hybrid multi-GPU computing: accelerated kernels for segmentation and object detection with medical image processing applications

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Abstract

In the last two decades, we have seen an amazing development of image processing techniques targeted for medical applications. We propose multi-GPU-based parallel real-time algorithms for segmentation and shape-based object detection, aiming at accelerating two medical image processing methods: automated blood detection in wireless capsule endoscopy (WCE) images and automated bright lesion detection in retinal fundus images. In the former method we identified segmentation and object detection as being responsible for consuming most of the global processing time. While in the latter, as segmentation was not used, shape-based object detection was the compute-intensive task identified. Experimental results show that the accelerated method running on multi-GPU systems for blood detection in WCE images is on average 265 times faster than the original CPU version and is able to process 344 frames per second. By applying the multi-GPU framework for bright lesion detection in fundus images we are able to process 62 frames per second with a speedup average 667 times faster than the equivalent CPU version.

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Acknowledgments

This work was partially supported by project PTDC/MATNAN/0593/2012 and also by CMUC and FCT (Portugal), through European program COMPETE/FEDER and project PEst-C/MAT/UI0324/2011. The work was also supported by Instituto de Telecomunicações under project UID/EEA/50008/2013 and carried out at the Multimedia Signal Processing Lab, a GPU/CUDA Research Center from the University of Coimbra.

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Authors and Affiliations

  1. Instituto de Telecomunicações, Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Coimbra, 3030-290, Coimbra, Portugal

    Carlos Graca & Gabriel Falcao

  2. CMUC, Department of Mathematics, University of Coimbra, 3001-501, Coimbra, Portugal

    Isabel N. Figueiredo

  3. Department of Applied Sciences, National Institute of Technology Delhi, Delhi, 110040, India

    Sunil Kumar

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  1. Carlos Graca
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  2. Gabriel Falcao
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  3. Isabel N. Figueiredo
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  4. Sunil Kumar
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Correspondence to Gabriel Falcao.

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Graca, C., Falcao, G., Figueiredo, I.N. et al. Hybrid multi-GPU computing: accelerated kernels for segmentation and object detection with medical image processing applications. J Real-Time Image Proc 13, 227–244 (2017). https://doi.org/10.1007/s11554-015-0517-3

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