About
Segmentation tools based on the graph cut algorithm. You can see video to get an idea. There are two algorithms implemented. Classic 3D Graph-Cut with regular grid and Multiscale Graph-Cut for segmentation of compact objects.
please cite:
@INPROCEEDINGS{jirik2013,
author = {Jirik, M. and Lukes, V. and Svobodova, M. and Zelezny, M.},
title = {Image Segmentation in Medical Imaging via Graph-Cuts.},
year = {2013},
journal = {11th International Conference on Pattern Recognition and Image Analysis: New Information Technologies (PRIA-11-2013). Samara, Conference Proceedings },
url = {http://www.kky.zcu.cz/en/publications/JirikMmjirik_2013_ImageSegmentationin},
}
Authors
- Miroslav Jirik
- Vladimir Lukes
Special requirements
See third party licenses
Resources
https://github.com/mjirik/imcut
https://github.com/amueller/gco_python
License
New BSD License, see the LICENSE file.
Install conda
conda install -c mjirik -c conda-forge imcut
pip install pygco
Install pip
pip install pygco imcut
See INSTALL file for more information
Run
Create output.mat file:
python imcut/dcmreaddata.py -i directoryWithDicomFiles --degrad 4
See data:
python imcut/seed_editor_qt.py -f output.mat
Make graph_cut:
python imcut/pycut.py -i output.mat
Use is as a library:
import numpy as np
import imcut.pycut as pspc
data = np.random.rand(30, 30, 30)
data[10:20, 5:15, 3:13] += 1
data = data * 30
data = data.astype(np.int16)
igc = pspc.ImageGraphCut(data, voxelsize=[1, 1, 1])
seeds = igc.interactivity()
More complex example without interactivity
import numpy as np
import imcut.pycut as pspc
import matplotlib.pyplot as plt
# create data
data = np.random.rand(30, 30, 30)
data[10:20, 5:15, 3:13] += 1
data = data * 30
data = data.astype(np.int16)
# Make seeds
seeds = np.zeros([30,30,30])
seeds[13:17, 7:10, 5:11] = 1
seeds[0:5:, 0:10, 0:11] = 2
# Run
igc = pspc.ImageGraphCut(data, voxelsize=[1, 1, 1])
igc.set_seeds(seeds)
igc.run()
# Show results
colormap = plt.cm.get_cmap('brg')
colormap._init()
colormap._lut[:1:,3]=0
plt.imshow(data[:, :, 10], cmap='gray')
plt.contour(igc.segmentation[:, :,10], levels=[0.5])
plt.imshow(igc.seeds[:, :, 10], cmap=colormap, interpolation='none')
plt.show()
Configuration
segparams = {
# 'method':'graphcut',
'method': 'graphcut',
'use_boundary_penalties': False,
'boundary_dilatation_distance': 2,
'boundary_penalties_weight': 1,
'modelparams': {
'type': 'gmmsame',
'fv_type': "fv_extern",
'fv_extern': fv_function,
'adaptation': 'original_data',
}
'mdl_stored_file': False,
}
mdl_stored_file: if this is set, load model from file
