Slice, Simplify and Stitch: Topology-Preserving Simplification Scheme for Massive Voxel Data

Author Hubert Wagner



PDF
Thumbnail PDF

File

LIPIcs.SoCG.2023.60.pdf
  • Filesize: 1.05 MB
  • 16 pages

Document Identifiers

Author Details

Hubert Wagner
  • University of Florida, Gainesville, Fl, USA

Acknowledgements

I would like to thank Herbert Edelsbrunner, Teresa Heiss, Kevin Knudson, Marian Mrozek, Georg Osang and Vanessa Robins for their helpful comments.

Cite AsGet BibTex

Hubert Wagner. Slice, Simplify and Stitch: Topology-Preserving Simplification Scheme for Massive Voxel Data. In 39th International Symposium on Computational Geometry (SoCG 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 258, pp. 60:1-60:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.SoCG.2023.60

Abstract

We focus on efficient computations of topological descriptors for voxel data. This type of data includes 2D greyscale images, 3D medical scans, but also higher-dimensional scalar fields arising from physical simulations. In recent years we have seen an increase in applications of topological methods for such data. However, computational issues remain an obstacle. We therefore propose a streaming scheme which simplifies large 3-dimensional voxel data - while provably retaining its persistent homology. We combine this scheme with an efficient boundary matrix reduction implementation, obtaining an end-to-end tool for persistent homology of large data. Computational experiments show its state-of-the-art performance. In particular, we are now able to robustly handle complex datasets with several billions voxels on a regular laptop. A software implementation called Cubicle is available as open-source: https://bitbucket.org/hubwag/cubicle.

Subject Classification

ACM Subject Classification
  • Theory of computation → Computational geometry
  • Mathematics of computing → Combinatorial algorithms
Keywords
  • Computational topology
  • topological data analysis
  • topological image analysis
  • persistent homology
  • persistence diagram
  • discrete Morse theory
  • algorithm engineering
  • implementation
  • voxel data
  • volume data
  • image data

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Ulrich Bauer. Ripser: efficient computation of vietoris-rips persistence barcodes. Journal of Applied and Computational Topology, 5(3):391-423, 2021. Google Scholar
  2. Ulrich Bauer, Michael Kerber, and Jan Reininghaus. Clear and compress: Computing persistent homology in chunks. In Topological Methods in Data Analysis and Visualization, 2014. Google Scholar
  3. Ulrich Bauer, Michael Kerber, and Jan Reininghaus. Distributed computation of persistent homology. In 2014 proceedings of the sixteenth workshop on algorithm engineering and experiments (ALENEX), pages 31-38. SIAM, 2014. Google Scholar
  4. Ulrich Bauer, Michael Kerber, Jan Reininghaus, and Hubert Wagner. Phat: Persistent homology algorithms toolbox. Journal of Symbolic Computation, 78:76-90, 2017. Algorithms and Software for Computational Topology. URL: https://doi.org/10.1016/j.jsc.2016.03.008.
  5. Ulrich Bauer, Talha Bin Masood, Barbara Giunti, Guillaume Houry, Michael Kerber, and Abhishek Rathod. Keeping it sparse: Computing persistent homology revised. arXiv preprint arXiv:2211.09075, 2022. Google Scholar
  6. Paul Bendich, Herbert Edelsbrunner, and Michael Kerber. Computing robustness and persistence for images. IEEE transactions on visualization and computer graphics, 16(6):1251-1260, 2010. Google Scholar
  7. Chao Chen and Michael Kerber. Persistent homology computation with a twist. In Proceedings 27th European workshop on computational geometry, volume 11, pages 197-200, 2011. Google Scholar
  8. Ronald H Cohen, William P Dannevik, Andris M Dimits, Donald E Eliason, Arthur A Mirin, Ye Zhou, David H Porter, and Paul R Woodward. Three-dimensional simulation of a richtmyer-meshkov instability with a two-scale initial perturbation. Physics of Fluids, 14(10):3692-3709, 2002. Google Scholar
  9. Olaf Delgado-Friedrichs, Vanessa Robins, and Adrian Sheppard. Skeletonization and partitioning of digital images using discrete morse theory. IEEE transactions on pattern analysis and machine intelligence, 37(3):654-666, 2014. Google Scholar
  10. Olaf Delgado-Friedrichs, Vanessa Robins, and Adrian Sheppard. Skeletonization and partitioning of digital images using discrete morse theory. IEEE transactions on pattern analysis and machine intelligence, 37(3):654-666, 2015. Google Scholar
  11. Roman Dementiev, Lutz Kettner, and Peter Sanders. Stxxl: standard template library for xxl data sets. Software: Practice and Experience, 38(6):589-637, 2008. Google Scholar
  12. Pawel Dlotko. Cubical complex. In GUDHI User and Reference Manual. GUDHI Editorial Board, 2015. URL: http://gudhi.gforge.inria.fr/doc/latest/group__cubical__complex.html.
  13. Herbert Edelsbrunner and John Harer. Computational topology: an introduction. American Mathematical Soc., 2010. Google Scholar
  14. Herbert Edelsbrunner, David Letscher, and Afra Zomorodian. Topological persistence and simplification. In Proceedings 41st annual symposium on foundations of computer science, pages 454-463. IEEE, 2000. Google Scholar
  15. Robin Forman. A user’s guide to discrete morse theory. Séminaire Lotharingien de Combinatoire [electronic only], 48:B48c-35, 2002. Google Scholar
  16. Adélie Garin, Teresa Heiss, Kelly Maggs, Bea Bleile, and Vanessa Robins. Duality in persistent homology of images. arXiv preprint arXiv:2005.04597, 2020. Google Scholar
  17. Charles Gueunet, Pierre Fortin, Julien Jomier, and Julien Tierny. Contour forests: Fast multi-threaded augmented contour trees. In 2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV), pages 85-92. IEEE, 2016. Google Scholar
  18. Pierre Guillou, Jules Vidal, and Julien Tierny. Discrete morse sandwich: Fast computation of persistence diagrams for scalar data-an algorithm and a benchmark. arXiv preprint arXiv:2206.13932, 2022. Google Scholar
  19. David Günther, Jan Reininghaus, Hubert Wagner, and Ingrid Hotz. Efficient computation of 3D morse-smale complexes and persistent homology using discrete morse theory. The Visual Computer, pages 1-11, 2012. Google Scholar
  20. Attila Gyulassy, Peer-Timo Bremer, Bernd Hamann, and Valerio Pascucci. A practical approach to morse-smale complex computation: Scalability and generality. IEEE Transactions on Visualization and Computer Graphics, 14(6), 2008. Google Scholar
  21. Teresa Heiss and Hubert Wagner. Streaming algorithm for Euler characteristic curves of multidimensional images. In Michael Felsberg, Anders Heyden, and Norbert Krüger, editors, Computer Analysis of Images and Patterns - 17th International Conference, CAIP, volume 10424 of Lecture Notes in Computer Science, pages 397-409. Springer, 2017. URL: https://doi.org/10.1007/978-3-319-64689-3_32.
  22. G. Henselman and R. Ghrist. Matroid Filtrations and Computational Persistent Homology. ArXiv e-prints, June 2016. URL: https://arxiv.org/abs/1606.00199.
  23. Xiaoling Hu, Fuxin Li, Dimitris Samaras, and Chao Chen. Topology-preserving deep image segmentation. In Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019. Google Scholar
  24. Xiaoling Hu, Yusu Wang, Li Fuxin, Dimitris Samaras, and Chao Chen. Topology-aware segmentation using discrete morse theory. arXiv preprint arXiv:2103.09992, 2021. Google Scholar
  25. Michael Joswig and Marc E Pfetsch. Computing optimal morse matchings. SIAM Journal on Discrete Mathematics, 20(1):11-25, 2006. Google Scholar
  26. Tomasz Kaczynski, Konstantin Mischaikow, and Marian Mrozek. Computational Homology. Springer-Verlag, New York, 2004. Google Scholar
  27. Shizuo Kaji, Takeki Sudo, and Kazushi Ahara. Cubical ripser: Software for computing persistent homology of image and volume data. arXiv preprint arXiv:2005.12692, 2020. Google Scholar
  28. Kevin Knudson and Bei Wang. Discrete stratified morse theory: Algorithms and a user’s guide. arXiv preprint arXiv:1801.03183, 2018. Google Scholar
  29. Jonathan Shewchuk Martin Isenburg. Streaming connected component computation for trillion voxel images. In Workshop on Massive Data Algorithmics, 2009. Google Scholar
  30. Konstantin Mischaikow and Marian Mrozek. Chaos in the lorenz equations: a computer-assisted proof. Bulletin of the American Mathematical Society, 32(1):66-72, 1995. Google Scholar
  31. Konstantin Mischaikow and Vidit Nanda. Morse theory for filtrations and efficient computation of persistent homology. Discrete & Computational Geometry, 50(2):330-353, 2013. Google Scholar
  32. Arnur Nigmetov and Dmitriy Morozov. Local-global merge tree computation with local exchanges. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1-13, 2019. Google Scholar
  33. Vanessa Robins, Peter John Wood, and Adrian P Sheppard. Theory and algorithms for constructing discrete morse complexes from grayscale digital images. IEEE Transactions on pattern analysis and machine intelligence, 33(8):1646-1658, 2011. Google Scholar
  34. Julien Tierny, Guillaume Favelier, Joshua A Levine, Charles Gueunet, and Michael Michaux. The topology toolkit. IEEE transactions on visualization and computer graphics, 24(1):832-842, 2017. Google Scholar
  35. Alessandro Verri, Claudio Uras, Patrizio Frosini, and Massimo Ferri. On the use of size functions for shape analysis. Biological cybernetics, 70(2):99-107, 1993. Google Scholar
  36. Hubert Wagner, Chao Chen, and Erald Vuçini. Efficient computation of persistent homology for cubical data. In Workshop on Topology-based Methods in Data Analysis and Visualization, 2011. Google Scholar
  37. Fan Wang, Saarthak Kapse, Steven Liu, Prateek Prasanna, and Chao Chen. Topotxr: A topological biomarker for predicting treatment response in breast cancer. In International Conference on Information Processing in Medical Imaging, pages 386-397. Springer, 2021. Google Scholar
  38. Fan Wang, Hubert Wagner, and Chao Chen. Gpu computation of the euler characteristic curve for imaging data. In 38th International Symposium on Computational Geometry (SoCG 2022). Schloss Dagstuhl-Leibniz-Zentrum für Informatik, 2022. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail