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A Fast Parallel Implementation of Queue-based Morphological Reconstruction using GPUs

George Teodoro, Tony Pan, Tahsin M. Kurc, Lee Cooper, Jun Kong, Joel H. Saltz
Center for Comprehensive Informatics, Emory University, Atlanta, GA 30322
Emory University, Center for Comprehensive Informatics Technical Report CCI-TR-2012-2, 2012

@article{teodoro2012fast,

   title={A Fast Parallel Implementation of Queue-based Morphological Reconstruction using GPUs},

   author={Teodoro, G. and Pan, T. and Kurc, T.M. and Cooper, L. and Kong, J. and Saltz, J.H.},

   year={2012}

}

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In this paper we develop and experimentally evaluate a novel GPU-based implementation of the morphological reconstruction operation. This operation is commonly used in the segmentation and feature computation steps of image analysis pipelines, and often used as a component in other image processing operations. Our implementation builds on a fast hybrid CPU algorithm, which employs a queue structure for efficient execution, and is the first GPU-enabled version of the queue-based hybrid algorithm. We evaluate our implementation using state-of-the-art GPU accelerators and images obtained by high resolution microscopy scanners from whole tissue slides. The experimental results show that the GPU version achieves upto 20 times faster processing compared to the CPU version and performs much better than a previously published implementation based on a slower sequential algorithm.
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