Image Processing in Python for 3D image stacks.
Image Processing in Python for 3D image stacks, or IMPPY3D, is a software repository comprising mostly Python scripts that simplify post-processing and 3D shape characterization of grayscale image stacks, otherwise known as volume-based images, 3D images, or voxel models. IMPPY3D was originally created for post-processing image stacks generated from X-ray computed tomography measurements. However, IMPPY3D also contains a functions to aid in post-processing general 2D/3D images.
Python was chosen for this library because of it is a productive, easy-to-use language. However, for computationally intense calculations, compiled codes and libraries are used for improved performance, such as well known libraries like Numpy and SciKit-Image. Compiled libraries internal to IMPPY3D were created using Cython. IMPPY3D was developed in an Anaconda environment with Windows 10 and Linux in mind, and suitable Anaconda environment files for these operating systems are provided to simplify the process of installing the necessary dependencies although some dependancy resolution may still be required.
Some of the highlighted capabilies of IMPPY3D include: interactive graphical user-interfaces (GUIs) available for many image processing functions, various 2D/3D image filters (e.g., blurring, sharpening, denoising, erosion/dilation), the ability to segment and label continuous 3D objects, precisely rotating an image stack in 3D and re-slicing along the new Z-axis, multiple algorithms available for fitting rotated bounding boxes to continuous voxel objects, image stacks can be converted into 3D voxel models suitable for viewing in ParaView, and voxel models can be represented as smooth surface-based models like STL meshes. Additional information and example scripts can be found in the included ReadMe files.
Synthetic X-ray computed tomography (X-ray CT) images can be created. To do so run the "./resources/generate_sample_data.py" script, after follwoing the setup instructions. Some examples may have been downloaded as part of the code, these are found in the "./examples/resources/powder_particles/" directory. This image stack imitates what actual X-ray CT data would look like of isolated, metal powder-particles commonly used in metal-based additive manufacturing.
Using IMPPY3D, the synthetic image stack can be denoised and segmented. Then, the segmented particles can be converted into voxel model. As a demonstration, these powder particles were characterized in terms of volume, porosity, orientation, aspect ratio, sphericity, and more. Additional information can be found in the example script, "./examples/segment_3d_particles/segment_3D_powder_particles.py".
The development of IMPPY3D uses the Miniforge package manager (Anaconda will work as well). To utilize IMPPY3D, you will need to install additional Python libraries. A full list of these Python dependencies can be found in the "./dependencies/" folder via YML text files. Step-by-step instructions on setting up a suitable Python environment, for either a Windows or Linux environment, can also be found in the "./dependencies/" folder. Generic installation files that utilize PIP are also provided for installing IMPPY3D on operating systems other than Windows or Linux.
A number of example Python scripts are provided in the "./examples/" folder to help facilitate rapid development of new projects. As we continue to use IMPPY3D in new applications, we aim to continue to provide new example scripts in this folder.
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Convert the comment blocks in function definitions to a common standard for automatic generation of the documentation using Sphinx.
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Incoporate additional libraries like TomoPy for reconstruction of X-ray CT radiographs and removal of ring artifacts.
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Create an optimization routine that stitches multiple X-ray CT fields-of-view together.
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Add more example scripts: converting a voxel model back to an image stack, characterizing voids and defects in additively manufactured metals, and so on.
If you encounter any bugs or unintended behavior, please create an "Issue" in the IMPPY3D GitHub repository and report a bug. You can also make a request for new features in this way.
For questions on how best to use IMPPY3D for a specific application, feel free to contact Dr. Newell Moser (see below).
- Dr. Newell Moser, NIST (newell.moser@nist.gov)
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Dr. Alexander K. Landauer, NIST
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Dr. Orion L. Kafka, NIST
- Dr. Edward J. Garboczi
If IMPPY3D has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following NIST data repository:
Moser, Newell H., Landauer, Alexander K., Kafka, Orion L. (2023), IMPPY3D: Image processing in python for 3D image stacks, National Institute of Standards and Technology, https://doi.org/10.18434/mds2-2806
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