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Cited by 19Medical Image Segmentation TechniquesParallel Computing and Optimization TechniquesDigital Image Processing Techniques

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GPU accelerated fuzzy connected image segmentation by using CUDA

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Ying Zhuge, Yong Cao, Miller, R.W.·2009·2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
DOI 10.1109/iembs.2009.5333158PMC PMC8381242
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Abstract

Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem of these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays commodity graphics hardware provides high parallel computing power. In this paper, we present a parallel fuzzy connected image segmentation algorithm on Nvidia's Compute Unified Device Architecture (CUDA) platform for segmenting large medical image data sets. Our experiments based on three data sets with small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 7.2x, 7.3x, and 14.4x, correspondingly, for the three data sets over the sequential implementation of fuzzy connected image segmentation algorithm on CPU.

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Research works printed but not linked: 19

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Metadata

Authors
Zhuge, Y., Cao, Y., & Miller, R. W.
Affiliations
  • National Institutes of Health (US)
  • Virginia Tech (US)
ORCID
Venue
2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Publisher
IEEE
Published
2009-09
Volume
2009
Pages
6341-6344
ISSN
2375-7477 (ISSN-L)
DOI
10.1109/iembs.2009.5333158
PMC
PMC8381242
OpenAlex
W2006512335
Cited by
19
References
0
Citations by year
2020: 1 · 2019: 1 · 2018: 1 · 2017: 2 · 2016: 2 · 2015: 2 · 2014: 2 · 2013: 4
FWCI
1.09
Type
conference paper
Language
en
Open access
Locations
  • Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institutes of Health
  • National Cancer Institute (ZIDSC006329, ZIASC006330)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Image Processing Techniques
Keywords
fuzzy connectedness, image segmentation, GPU acceleration, CUDA, parallel computing, medical image segmentation, large-scale image databases, speedup
MeSH terms
Algorithms, Computer Graphics, Equipment Design, Image Enhancement, Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Time Factors, Reproducibility of Results, Fuzzy Logic, Equipment Failure Analysis

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CoChat. (2026). Citation check: GPU accelerated fuzzy connected image segmentation by using CUDA. https://app.cochat.ai/papers/10.1109/iembs.2009.5333158
The paper
Zhuge, Y., Cao, Y., & Miller, R. W. (2009). GPU accelerated fuzzy connected image segmentation by using CUDA. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. https://doi.org/10.1109/iembs.2009.5333158