We present a method of image harmonization using Cumulative Distribution Function (CDF) matching based on curve fitting.
This approach does not ruin local variability and individual important features. The transformation of image intensities is non-linear but still “smooth and elastic”, as compared to other known histogram matching algorithms. Nonlinear transformation allows for a very good match to the template. At the same time, elasticity constraints help to preserve local variability among individual inputs, which may encode important features for subsequent machine-learning processing. The pre-defined template CDF offers a better and more intuitive control for the input data transformation compared to other methods, especially ML-based ones. Even though we developed our method for MRI images, the method is generic enough to apply to other types of imaging data.
... :: TBD :: ...
The library can be downloaded from the GitLab. See the link above to go to the GitLab project site.
If you use this tool, please cite the following paper:
This project was partially funded by the Ministry of Health of the Czech Republic (Grant No. NU21-08-00359) and the Ministry of Education, Youth and Sports of the Czech Republic (Project No. LM2023050).