Skip to main content

Fast local thickness in 3D and 2D.

Project description

localthickness

Fast local thickness in 3D and 2D. Implements the algorithm described in our CVPR-W (CVMI) paper.

Installation

Install the module using pip install localthickness or clone the repository.

Use

import localthickness as lt

#  Make a binary test volume. 
B = lt.create_test_volume((100, 500, 400), sigma=15, boundary=0.001)

# Compute thickness and separation.
thickness = lt.local_thickness(B, scale=0.5)
separation = lt.local_thickness(~B, scale=0.5)

# Visualize.
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 3, figsize=(10, 5))
ax[0].imshow(B[10])
ax[1].imshow(thickness[10], cmap=lt.black_plasma())
ax[2].imshow(separation[10], cmap=lt.white_viridis())

Paper

The fast local thickness method is described and evaluated in our contribution to the 8th IEEE Workshop on Computer Vision for Microscopy Image Analysis (CVMI), held in conjunction with the CVPR 2023 conference. Please cite our paper if you use the method in your work.

@inproceedings{dahl2023fast,
  title={Fast Local Thickness},
  author={Dahl, Vedrana Andersen and Dahl, Anders Bjorholm},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  pages={4335--4343},
  year={2023}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

localthickness-0.1.3.tar.gz (19.7 kB view hashes)

Uploaded Source

Built Distribution

localthickness-0.1.3-py3-none-any.whl (20.0 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page