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Tytuł:
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Fast parallel vessel segmentation.
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Autorzy:
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Satpute N; Department of Electronic and Computer Engineering, Universidad de Córdoba, Spain. Electronic address: .
Naseem R; Norwegian Colour and Visual Computing Lab, Norwegian University of Science and Technology, Norway.
Palomar R; The Intervention Centre, Oslo University Hospital, Norway.
Zachariadis O; Department of Electronic and Computer Engineering, Universidad de Córdoba, Spain.
Gómez-Luna J; Department of Computer Science, ETH Zurich, Switzerland.
Cheikh FA; Norwegian Colour and Visual Computing Lab, Norwegian University of Science and Technology, Norway.
Olivares J; Department of Electronic and Computer Engineering, Universidad de Córdoba, Spain.
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Źródło:
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Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2020 Aug; Vol. 192, pp. 105430. Date of Electronic Publication: 2020 Mar 03.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Publication: Limerick : Elsevier Scientific Publishers
Original Publication: Amsterdam : Elsevier Science Publishers, c1984-
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MeSH Terms:
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Computer Graphics*
Image Processing, Computer-Assisted/*methods
Liver/*diagnostic imaging
Algorithms
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Contributed Indexing:
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Keywords: GPU; Grid-stride loop; Kernel termination and relaunch (KTRL); Persistent; Seeded region growing
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Entry Date(s):
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Date Created: 20200315 Date Completed: 20210414 Latest Revision: 20210414
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Update Code:
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20240105
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DOI:
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10.1016/j.cmpb.2020.105430
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PMID:
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32171150
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Background and Objective: Accurate and fast vessel segmentation from liver slices remain challenging and important tasks for clinicians. The algorithms from the literature are slow and less accurate. We propose fast parallel gradient based seeded region growing for vessel segmentation. Seeded region growing is tedious when the inter connectivity between the elements is unavoidable. Parallelizing region growing algorithms are essential towards achieving real time performance for the overall process of accurate vessel segmentation.
Methods: The parallel implementation of seeded region growing for vessel segmentation is iterative and hence time consuming process. Seeded region growing is implemented as kernel termination and relaunch on GPU due to its iterative mechanism. The iterative or recursive process in region growing is time consuming due to intermediate memory transfers between CPU and GPU. We propose persistent and grid-stride loop based parallel approach for region growing on GPU. We analyze static region of interest of tiles on GPU for the acceleration of seeded region growing.
Results: We aim fast parallel gradient based seeded region growing for vessel segmentation from CT liver slices. The proposed parallel approach is 1.9x faster compared to the state-of-the-art.
Conclusion: We discuss gradient based seeded region growing and its parallel implementation on GPU. The proposed parallel seeded region growing is fast compared to kernel termination and relaunch and accurate in comparison to Chan-Vese and Snake model for vessel segmentation.
(Copyright © 2020. Published by Elsevier B.V.)
