research-article

Streamed watershed transform on GPU for processing of large volume data

Authors:

Michal Hučko,

Miloš ŠrámekAuthors Info & Claims

SCCG '12: Proceedings of the 28th Spring Conference on Computer Graphics

Pages 137 - 141

https://doi.org/10.1145/2448531.2448549

Published: 02 May 2012 Publication History

Get Access

Abstract

Since its introduction the watershed transform became a popular method for volume data segmentation. A range of various algorithms for its computation were developed, including parallel algorithms for computation on different architectures. Recently also algorithms for consumer graphical accelerators were developed. Neither of these, however, are able to process data larger than the available memory as the whole data has to be present in the memory of the device. In this paper we present two versions of a streamed multi-pass algorithm for watershed computation on a GPU. As the slice-based streaming approach is used both variants are capable of processing data exceeding the size of the available graphics accelerator memory.

References

[1]

Bieniek, A., Burkhardt, H., ludwigs-universitat Freiburg, A., Marschner, H., Schreiber, G., I, T. I., and Nolle, M. 1997. A parallel watershed algorithm. In In Proc. 10th Scandinavian Conference on Image Analysis (SCIA'97, 237--244.

Google Scholar

[2]

Digabel, H., and Lantuejoul, C. 1978. Iterative algorithms. In Proceedings of 2nd European Symposium on Quantitative Analysis of Microstructures in Material Science, 85--99.

Google Scholar

[3]

Hučko, M., and Šrámek, M. 2011. Region-based processing of volumetric data. Medical Informatics & Technologies 17.

Google Scholar

[4]

Kauffmann, C., and Piché, N. 2008. Cellular automaton for ultra-fast watershed transform on gpu. In Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, 1--4.

Google Scholar

[5]

Kauffmann, C., and Piché, N. 2010. Seeded nd medical image segmentation by cellular automaton on gpu. International Journal of Computer Assisted Radiology and Surgery 5, 251--262. 10.1007/s11548-009-0392-0.

Crossref

Google Scholar

[6]

Körbes, A., Lotufo, R., Vitor, G., and Ferreira, J. 2009. A Proposal for a Parallel Watershed Transform Algorithm for Real-Time Segmentation.

Google Scholar

[7]

Körbes, A., Vitor, G., de Alencar Lotufo, R., and Ferreira, J. 2011. Advances on watershed processing on gpu architecture. In Mathematical Morphology and Its Applications to Image and Signal Processing, P. Soille, M. Pesaresi, and G. Ouzounis, Eds., vol. 6671 of Lecture Notes in Computer Science. Springer Berlin / Heidelberg, 260--271.

Digital Library

Google Scholar

[8]

Law, C. C., Schroeder, W. J., Martin, K. M., and Temkin, J. 1999. A multi-threaded streaming pipeline architecture for large structured data sets. In VIS '99: Proceedings of the conference on Visualization '99, IEEE Computer Society Press, Los Alamitos, CA, USA, 225--232.

Digital Library

Google Scholar

[9]

Pan, L., Gu, L., and Xu, J. 2008. Implementation of medical image segmentation in cuda. In Information Technology and Applications in Biomedicine, 2008. ITAB 2008. International Conference on, 82--85.

Google Scholar

[10]

Roerdink, and Meijster. 2000. The watershed transform: Definitions, algorithms and parallelization strategies. FUNDINF: Fundamenta Informatica 41.

Digital Library

Google Scholar

[11]

Šrámek, M., Dimitrov, L. I., Straka, M., and Červeňanský, M. 2004. The f3d tools for processing and visualization of volumetric data. Journal of Medical Informatics and Technologies, MIP-71--MIP-79.

Google Scholar

[12]

Varchola, A., Vaško, A., Solčány, V., Dimitrov, L. I., and Šrámek, M. 2007. Processing of volumetric data by slice-and process-based streaming. In AFRIGRAPH '07: Proceedings of the 5th international conference on Computer graphics, virtual reality, visualisation and interaction in Africa, ACM, New York, NY, USA, 101--110.

Digital Library

Google Scholar

[13]

Wagner, B., Dinges, A., Müller, P., and Haase, G. 2009. Parallel volume image segmentation with watershed transformation. In Image Analysis, A.-B. Salberg, J. Hardeberg, and R. Jenssen, Eds., vol. 5575 of Lecture Notes in Computer Science. Springer Berlin / Heidelberg, 420--429.

Digital Library

Google Scholar

[14]

Young, I. T., and van Vliet, L. J. 1995. Recursive implementation of the gaussian filter. Signal Process. 44 (June), 139--151.

Digital Library

Google Scholar

Cited By

View all

Kornilov ASafonov IYakimchuk I(2022)A Review of Watershed Implementations for Segmentation of Volumetric ImagesJournal of Imaging10.3390/jimaging80501278:5(127)Online publication date: 26-Apr-2022
https://doi.org/10.3390/jimaging8050127
Gabrielyan YYeghiazaryan VVoiculescu I(2022)Parallel Partitioning: Path Reducing and Union–Find Based Watershed for the GPU2022 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP46576.2022.9897372(1501-1505)Online publication date: 16-Oct-2022
https://doi.org/10.1109/ICIP46576.2022.9897372
Stojanovic NStojanovic D(2019)Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACCISPRS International Journal of Geo-Information10.3390/ijgi80903868:9(386)Online publication date: 3-Sep-2019
https://doi.org/10.3390/ijgi8090386
Show More Cited By

Index Terms

Streamed watershed transform on GPU for processing of large volume data
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation
      1. Image processing

Recommendations

Modified Watershed Transform for Automated Brain Segmentation from Magnetic Resonance Images
ICCCV '19: Proceedings of the 2nd International Conference on Control and Computer Vision

The segmentation of human brain from Magnetic Resonance Image (MRI) is one of the most important parts of clinical diagnostic. Brains' anatomical structures can be visualized and measured through image segmentation. Especially, while clinical analysis ...
An improved approach of lung image segmentation based on watershed algorithm
ICIMCS '15: Proceedings of the 7th International Conference on Internet Multimedia Computing and Service

As a preprocessing step of chest Computed Tomography (CT) images, lung segmentation is significant for the diagnosis of lung disease. The traditional watershed algorithm is sensitive to the noise and has the drawback of over-segmentation problem. This ...
Segmentation of Brain Structures by Watershed Transform on Tensorial Morphological Gradient of Diffusion Tensor Imaging
SIBGRAPI '09: Proceedings of the 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing

Watershed transform on tensorial morphological gradient (TMG) is a new approach to segment diffusion tensor images (DTI). Since the TMG is able to express the tensorial dissimilarities in a single scalar image, the segmentation problem of DTI is then ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

SCCG '12: Proceedings of the 28th Spring Conference on Computer Graphics

March 2013

158 pages

ISBN:9781450319775

DOI:10.1145/2448531

Conference Chair:
Carol O'Sullivan
Trinity College Dublin, Ireland

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 May 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Science Grant Agency (VEGA)
Agentúra na Podporu Výskumu a Vývoja

Conference

SCCG'12

Sponsor:

Comenius University
SIS

SCCG'12: Spring Conference on Computer Graphics

May 2 - 4, 2012

Budmerice, Slovakia

Acceptance Rates

Overall Acceptance Rate 67 of 115 submissions, 58%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
105
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)1

Reflects downloads up to 21 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Kornilov ASafonov IYakimchuk I(2022)A Review of Watershed Implementations for Segmentation of Volumetric ImagesJournal of Imaging10.3390/jimaging80501278:5(127)Online publication date: 26-Apr-2022
https://doi.org/10.3390/jimaging8050127
Gabrielyan YYeghiazaryan VVoiculescu I(2022)Parallel Partitioning: Path Reducing and Union–Find Based Watershed for the GPU2022 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP46576.2022.9897372(1501-1505)Online publication date: 16-Oct-2022
https://doi.org/10.1109/ICIP46576.2022.9897372
Stojanovic NStojanovic D(2019)Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACCISPRS International Journal of Geo-Information10.3390/ijgi80903868:9(386)Online publication date: 3-Sep-2019
https://doi.org/10.3390/ijgi8090386
Romero-Zaliz RReinoso-Gordo J(2017)An Updated Review on Watershed AlgorithmsSoft Computing for Sustainability Science10.1007/978-3-319-62359-7_12(235-258)Online publication date: 14-Jul-2017
https://doi.org/10.1007/978-3-319-62359-7_12
Quesada-Barriuso PB. Heras DArgüello F(2013)Efficient 2D and 3D watershed on graphics processing unitComputers and Electrical Engineering10.1016/j.compeleceng.2013.04.02039:8(2638-2655)Online publication date: 1-Nov-2013
https://dl.acm.org/doi/10.1016/j.compeleceng.2013.04.020

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Modified Watershed Transform for Automated Brain Segmentation from Magnetic Resonance Images

An improved approach of lung image segmentation based on watershed algorithm

Segmentation of Brain Structures by Watershed Transform on Tensorial Morphological Gradient of Diffusion Tensor Imaging