Cross-platform 2d/3d image segmentation C++ library
Authors: Roman V. Vasilyev, Timofey Sizonenko, Kirill M. Gerke, Marina V. Karsanina, Andrey A. Ananev Moscow, 2017-2024
Mac OS:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" && xcode-select --install
Linux (Ubuntu):
sudo apt-get install g++
Windows: Visual Studio 2015 or later with C++ tools installed
The easiest way to install and run MAZAlib is to utilize Python virtual environments (below are the commands to setup MAZAlib in Unix-based operation system, similar approach can be applied under Windows):
- Here env_name is any environment name that will used to run MAZAlib. path_to_env - path to the environment where it will be created (Use '.') if it's current folder.
python -m venv path_to_env/env_name
source path_to_env/env_name/bin/activate
- Install MAZAlib:
pip install mazalib
Script test.py contains all implemented methods. Also you can write your logic using this script as example
Example usage as Python package:
import numpy as np
import mazalib
im = np.fromfile('path/to/image.raw', dtype='uint8')
side_size = int(round(im.size**(1/3)))
im = np.reshape(im, (side_size,side_size,side_size))
result = mazalib.unsharp(im, [3.0])
Binarization (segmentation into two phases):
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Indicator kriging (Oh, W., & Lindquist, B. (1999). Image thresholding by indicator kriging. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(7), 590–602) link
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Markov random field aka MRF (Kulkarni, R., Tuller, M., Fink, W., & Wildenschild, D. (2012). Three-dimensional multiphase segmentation of X-ray CT data of porous materials using a Bayesian Markov random field framework. Vadose Zone Journal, 11(1)) link
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Region growth aka RG (Hashemi, M. A., Khaddour, G., François, B., Massart, T. J., & Salager, S. (2014). A tomographic imagery segmentation methodology for three-phase geomaterials based on simultaneous region growing. Acta Geotechnica, 9, 831-846) link
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Converging active contours aka CAC (Sheppard, A. P., Sok, R. M., & Averdunk, H. (2004). Techniques for image enhancement and segmentation of tomographic images of porous materials. Physica A: Statistical mechanics and its applications, 339(1-2), 145-151) link
Filters:
- Unsharp mask
- Non-local means (we added NLM implementation by Bruns, S., Stipp, S. L. S., & Sørensen, H. O. (2017). Looking for the signal: A guide to iterative noise and artefact removal in X-ray tomographic reconstructions of porous geomaterials. Advances in Water Resources, 105, 96-107) link
The paper describing this library is currently submitted to Computers & Geosciences Journal.
- MAZAgui - to utilize graphical user interface for MAZAlib
- PyFDMSS - a library to simulate single-phase flow on binary 3D pore geometries (Gerke, K. M., Vasilyev, R. V., Khirevich, S., Collins, D., Karsanina, M. V., Sizonenko, T. O., Korost, D.V., Lamontagne, S. & Mallants, D. (2018). Finite-difference method Stokes solver (FDMSS) for 3D pore geometries: Software development, validation and case studies. Computers & geosciences, 114, 41-58) link : pyfdmss