The Maximum Spacing Noise Estimation in Single-coil Background MRI Data
New noise estimation technique in single-coil background MRI data based on the maximum spacing estimation (MSP) principle for Rayleigh distributed data.
This MATLAB codes include implementations of the MSP algorithm and its variants:
- MSP estimator based on Kullback-Leibler divergence,
- MSP estimator based on J-divergence,
- MSP estimator based on Renyi divergence,
- MSP estimator based on Vajda's divergence.
Moreover, we supply MATLAB implementations of the previous literature reports:
- Aja's method,
- Brummer's method,
- Brummer-Aja's method,
- Chang's method,
- Maximum likelihood principle,
- Sijbers's method,
- Sijbers-Aja's method.
IMPORTANT NOTICE!
--------------------------------------
You can use these codes freely in your research/work, however, cite the following paper, please:
Pieciak, T. The Maximum Spacing Noise Estimation in Single-coil Background MRI Data. In: Image Processing (ICIP), 2014 21th IEEE International Conference on. IEEE, 2014. p. 1743-1747.
The draft of the paper can be downloaded at:
http://home.agh.edu.pl/pieciak/publikacje_pieciak/2014_ICIP_Pieciak.pdf
Cite As
Tomasz Pieciak (2024). The Maximum Spacing Noise Estimation in Single-coil Background MRI Data (https://www.mathworks.com/matlabcentral/fileexchange/45236-the-maximum-spacing-noise-estimation-in-single-coil-background-mri-data), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform Compatibility
Windows macOS LinuxCategories
- Sciences > Neuroscience > Human Brain Mapping > MRI >
Tags
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Discover Live Editor
Create scripts with code, output, and formatted text in a single executable document.