Stabilizing Tensor Voting for 3D Curvature Estimation

11 pages•Published: December 11, 2024

Victor Alfaro Pérez, Virginie Uhlmann and Brigitte Forster-Heinlein

Abstract

Curvature plays an important role in the function of biological membranes, and is therefore a readout of interest in microscopy data. The PyCurv library established itself as a valuable tool for curvature estimation in 3D microscopy images. However, in noisy images, the method exhibits visible instabilities, which are not captured by the standard error measures. In this article, we investigate the source of these instabilities, provide ade- quate measures to detect them, and introduce a novel post-processing step which corrects the errors. We illustrate the robustness of our enhanced method over various noise regimes and demonstrate that with our orientation correcting post-processing step, the PyCurv library becomes a truly stable tool for curvature quantification.

Keyphrases: curvature estimation, error measures, normal estimation, pycurv, surface orientation, tensor voting

In: Varvara L Turova, Andrey E Kovtanyuk and Johannes Zimmer (editors). Proceedings of 3rd International Workshop on Mathematical Modeling and Scientific Computing, vol 104, pages 14-24.

Links:	https://easychair.org/publications/paper/ssD6
	https://doi.org/10.29007/4nh8

BibTeX entry

@inproceedings{MMSC2024:Stabilizing_Tensor_Voting_3D,
  author    = {Victor Alfaro Pérez and Virginie Uhlmann and Brigitte Forster-Heinlein},
  title     = {Stabilizing Tensor Voting for 3D Curvature Estimation},
  booktitle = {Proceedings of  3rd International Workshop on Mathematical Modeling and Scientific Computing},
  editor    = {Varvara L Turova and Andrey E Kovtanyuk and Johannes Zimmer},
  series    = {EPiC Series in Computing},
  volume    = {104},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/ssD6},
  doi       = {10.29007/4nh8},
  pages     = {14-24},
  year      = {2024}}

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