Abstract
The Distance-Regularized Level Set Evolution (DRLSE) algorithm solves many problems that plague the class of Level Set algorithms, but has a significant computational cost and is sensitive to its many parameters. Configuring these parameters is a time-intensive trial-and-error task that limits the usability of the algorithm. This is especially true in the field of Medical Imaging, where it would be otherwise highly suitable. The aim of this work is to develop a parallel implementation of the algorithm using the Compute-Unified Device Architecture (CUDA) for Graphics Processing Units (GPU), which would reduce the computational cost of the algorithm, bringing it to the interactive regime. This would lessen the burden of configuring its parameters and broaden its application. Using consumer-grade, hardware, we observed performance gains between roughly 800% and 1700% when comparing against a purely serial C++ implementation we developed, and gains between roughly 180% and 500%, when comparing against the MATLAB reference implementation of DRLSE, both depending on input image resolution.
The results published here are in part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance Code 001.
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Coelho, D.P., Furuie, S.S. (2020). Parallel Implementation of the DRLSE Algorithm. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12132. Springer, Cham. https://doi.org/10.1007/978-3-030-50516-5_3
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DOI: https://doi.org/10.1007/978-3-030-50516-5_3
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