gaara

Skeleton thinning algorithm for large images.

import gaara
import numpy as np

arr = np.load(...) # some 3d array in fortran order

# binary images are more efficient to process
# 80% of the memory and faster.

# in_place: modify the input array in_place (saves memory)
# binary_image: consider the input image a binary image (foreground/bg only)
#   regardless of values. 0 is background.
# preserve_endpoints: mark endpoints 3x3x3 stencils containing exactly 2 foreground voxels
#   for preservation. By default, the palagyi algorithm is more aggressive.

arr = gaara.skeletonize(arr, binary_image=True, in_place=False, preserve_endpoints=False)
arr = gaara.skeletonize(arr, binary_image=False, in_place=False, preserve_endpoints=False)

Gaara is a skeletion generation via voxel thinning algorithm based on Palágyi's 2014 paper [1] and inspired by Matejek et al.'s work on synapseaware. [2]

While efficient and fine for tracing neurites that have a natural tree structure, a shortcoming of Kimimaro is its inability to generate topologically correct skeletons [3] which have applications to e.g. glia and blood vessels.

Gaara attempts to be an efficient voxel thinning algorithm implementation that can handle very large images on a single machine by making use of crackle compression dynamically.

Installation

The PyPI page is located here.

pip install gaara

If you are installing from source, ensure the lookup tables have been generated (they should be stored in version control). You can regenerate them using make in the lookup_tables directory. It takes about a minute to compute them.

References

1. K. Palágyi et al., “A Sequential 3D Thinning Algorithm and Its Medical Applications,” in Information Processing in Medical Imaging, M. F. Insana and R. M. Leahy, Eds., Berlin, Heidelberg: Springer, 2001, pp. 409–415. doi: 10.1007/3-540-45729-1_42.

2. B. Matejek, D. Wei, X. Wang, J. Zhao, K. Palágyi, and H. Pfister, “Synapse-Aware Skeleton Generation for Neural Circuits,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, D. Shen, T. Liu, T. M. Peters, L. H. Staib, C. Essert, S. Zhou, P.-T. Yap, and A. Khan, Eds., Cham: Springer International Publishing, 2019, pp. 227–235. doi: 10.1007/978-3-030-32239-7_26.

3. T. A. Syed, M. Youssef, A. L. Schober, Y. Kubota, K. K. Murai, and C. K. Salmon, “Beyond Neurons: Computer Vision Methods for Analysis of Morphologically Complex Astrocytes,” Frontiers in Computer Science, vol. 6, Sep. 2024, doi: 10.3389/fcomp.2024.1156204.