Scalable Simple Linear Iterative Clustering (SSLIC) Using a Generic and Parallel Approach
| Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3596 |
Published in The Insight Journal - 2018 January-December.
Submitted by Bradley Lowekamp on 07-23-2018.
Superpixel algorithms have proven to be a useful initial step for segmentation and subsequent processing of images, reducing computational complexity by replacing the use of expensive per-pixel primitives with a higher-level abstraction, superpixels. They have been successfully applied both in the context of traditional image analysis and deep learning based approaches. In this work, we present a general- ized implementation of the simple linear iterative clustering (SLIC) superpixel algorithm that has been generalized for n-dimensional scalar and multi-channel images. Additionally, the standard iterative im- plementation is replaced by a parallel, multi-threaded one. We describe the implementation details and analyze its scalability using a strong scaling formulation. Quantitative evaluation is performed using a 3D image, the Visible Human cryosection dataset, and a 2D image from the same dataset. Results show good scalability with runtime gains even when using a large number of threads that exceeds the physical number of available cores (hyperthreading).
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| Categories: | Filtering, Parallelization, SMP, Segmentation |
| Keywords: | Segmentation, Superpixels, Parallel, Performance |
| Tracking Number: | This work was supported by the Intramural Research Program of the U.S. National Institutes of Health, National Library of Medicine. |
| Toolkits: | ITK |
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