MRI image normalization scheme based on Rice distribution
RicianNormalization is a tool that reads T2W images (as DICOM or Nifti, MetaIO, etc...) and potentially other magnitude MR images, fits a Rice distribution to the pixel intensities and normalizes the pixels based on the standard score using the fit Rice mean and standard deviation.
PyRicianNormalization is a Python port of RicianNormalization
https://github.com/nslay/RicianNormalization
The method is loosely based on this work: Lemaître, Guillaume, et al. "Normalization of t2w-mri prostate images using rician a priori." Medical Imaging 2016: Computer-Aided Diagnosis. Vol. 9785. International Society for Optics and Photonics, 2016.
Though the implementation is vastly different employing a maximum log likelihood scheme to fit the Rice distribution.
TODO
TODO
Saving floating point voxel output to DICOM series does not seem to work correctly. Limitation of SimpleITK?