Machine learning approach to quantitative analysis for dynamic CT images of methane hydrate formation in sandy samples
by Mikhail I. Fokin, Viktor V. Nikitin and Anton A. Duchkov
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data.py: (file contains data processing functions)
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IO.py: (file contains functions for saving and loading GMM models)
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segmentation.py: (file contains functions for segmentation using U-net and GMM models)
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Unet_models.py: (file contains definition of 2D and 3D U-net models)
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train_models.py: (example of U-nets training)
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dataset_segmentationipynb: (example of using two step segmentation algorithm)
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dynamic_segmentation.ipynb: (example of using two step segmentation algorithm for dynamic data).
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Python (version 3.8)
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tensorflow (2.7.0)
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numpy (1.17.1)
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scipy (1.1.0)
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scikit-learn (0.22.1)
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matplotlib (3.1.1)
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tqdm (4.63.0)
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opencv-python (4.5.5.64)
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h5py (3.6.0)
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tifffile (2022.3.16)
BSD 3-Clause License
Copyright (c) 2022, Mikhail I. Fokin All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
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Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
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Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
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