This repository is the official implementation of the article "Estimation of Joint Local Intensity Distribution for Multi-Modal Image Registration".
To install requirements, please use pip
to install:
pip install -r requirements.txt
One can directly use the brainweb
dataset provided in the repository, or fetch new ones on its website.
For the MS-CMR
dataset, one should apply the dataset on its homepage.
The major implementations of LIC and GASD are provided in the file color_transfer.py
.
One can inspect the *.log
files and use the *.ckpt
files consisting of the state dictionaries for Exp3
in Table 4, manuscript.
To run the experiments, one should use the following command,
cd Exp124\ LIC_GASD
python3 Exp1_LIC_GASD.py --exp <experiment name>
for experiments in Tables 1 and 2.
Use commands,
cd Exp124\ LIC_GASD
python3 Exp1_LIC_GASD_same_modality.py --exp <experiment name>
for the experiments in Table 3.
The experiment names
are listed in the dictionary Experiments
in the Python files.
One can use the following options to design the experiments on his own.
--n_dim # the spatial dimension of the used dataset
--loss # the loss function; selected from LIC, GASD_only, SSD, NVI, MI, NMI, NCC, or the local versions including NVI_local, MI_local, NCC_local, or the losses after image translation: GASD, CTr_LIC
--n_batch # the batch size for paralleled processing
--n_iter # the number of iterations (for the iterative method)
--sig_p # the strength of the partial volume effect, $sigma_p$
--noise # the strength of noise added on input images
--down_sample # the downscaling factor
--exp # experiment name
Use commands,
cd Exp3_LIC_deep
python3 Exp3_LIC_deep.py --exp <experiment name>
for the experiments in Table 4.
One can design the experiments on his own by the following new options.
--n_epoch # the number of training epochs
--n_epoch_save # the period of model saving, in the unit of epoch numbers
--offset_scale # the scaling of the predicted displacements
This code is under CC BY 4.0 licence.
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