QIM tools and user interfaces for volumetric imaging
Project description
Quantitative Imaging in 3D
The qim3d (kɪm θriː diː) library is designed to make it easier to work with 3D imaging data in Python. It offers a range of features, including data loading and manipulation, image processing and filtering, visualization of 3D data, and analysis of imaging results.
You can easily load and process 3D image data from various file formats, apply filters and transformations to the data, visualize the results using interactive plots and 3D rendering, and perform quantitative analysis on the images.
Documentation available at https://platform.qim.dk/qim3d/
For more information on the QIM center visit https://qim.dk/
Installation
We recommned using a conda environment:
conda create -n qim3d python=3.11
After the environment is created, activate it by running:
conda activate qim3d
And then installation is easy using pip:
pip install qim3d
Remember that the environment needs to be activated each time you use qim3d!
For more detailed instructions and troubleshooting, please refer to the documentation.
Examples
Interactive volume slicer
import qim3d
vol = qim3d.examples.bone_128x128x128
qim3d.viz.slicer(vol)
Line profile
import qim3d
vol = qim3d.examples.bone_128x128x128
qim3d.viz.line_profile(vol)
Threshold exploration
import qim3d
# Load a sample volume
vol = qim3d.examples.bone_128x128x128
# Visualize interactive thresholding
qim3d.viz.threshold(vol)
Synthetic data generation
import qim3d
# Generate synthetic collection of volumes
num_volumes = 15
volume_collection, labels = qim3d.generate.volume_collection(num_volumes = num_volumes)
# Visualize the collection
qim3d.viz.volumetric(volume_collection)
Structure tensor analysis
import qim3d
vol = qim3d.examples.NT_128x128x128
val, vec = qim3d.processing.structure_tensor(vol, visualize = True, axis = 2)
Support
The development of the qim3d is supported by the Infrastructure for Quantitative AI-based Tomography QUAITOM which is supported by a Novo Nordisk Foundation Data Science Programme grant (Grant number NNF21OC0069766).
