  A spatially regularized Gaussian mixture model, LapGM, is proposed for the
bias field correction and magnetic resonance normalization problem. The
proposed spatial regularizer gives practitioners fine-tuned control between
balancing bias field removal and preserving image contrast preservation for
multi-sequence, magnetic resonance images. The fitted Gaussian parameters of
LapGM serve as control values which can be used to normalize image intensities
across different patient scans. LapGM is compared to well-known debiasing
algorithm N4ITK in both the single and multi-sequence setting. As a
normalization procedure, LapGM is compared to known techniques such as: max
normalization, Z-score normalization, and a water-masked region-of-interest
normalization. Lastly a CUDA-accelerated Python package $\texttt{lapgm}$ is
provided from the authors for use.
