qim3d 1.5.3

QIM tools and user interfaces for volumetric imaging

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://docs.qim.dk/qim3d/

For more information on the QIM center visit https://qim.dk/

Installation

We recommend 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.fibers_150x150x150
val, vec = qim3d.processing.structure_tensor(vol, visualize = True, axis = 1)

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).