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GPU accelerated fuzzy connected image segmentation by using CUDA
Open accessAbstract
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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“CUDA kullanılan uygulamalarda 2.5 kata kadar hızlanma sağlanmıştır.”
From GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi (2020) · cites this paper as [53]
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Research works printed but not linked: 19
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Cited by
Sorted by citations
GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi
AYDIN, S., SAMET, R., & BAY, Ö. F. (2020) · Politeknik Dergisi · doi:10.2339/politeknik.563767 · Cited by 3
“Tıbbi veriler üzerinde uygulanan başka bir çalışma da Zhuge ve diğerlerinin [54] yaptıkları çalışmadır.”
Background
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)
- 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
- green
- www.ncbi.nlm.nih.gov
- 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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