MRI_k-space-derived_details_edges

k-space based details/edges detection in MRI images with optional k-space based denoising and detail control
(data import suitable for Agilent FID files, three binarization methods to choose from).

Figure A. Sample results for details/edges detection with detail control and simple thresholding.

Figure B. Sample results for details/edges detection with denoising, detail control and adaptive thresholding.

The repository contains:

1. Python script kspace_det_edg.py.
2. Description.pdf presenting:

• short introduction to the topic,
• how the code works step by step,
• sample results with commentary.

3. Sample FID data in the sems_20190203_03.fid folder.
4. Sample results illustrations in FigureA.png and FigureB.png.

References

1. Collection of the sample data:

Beata Wereszczyńska, Alcohol-fixed specimens for high-contrast post-mortem MRI, Forensic Imaging, Volume 25, 2021, 200449, ISSN 2666-2256, https://doi.org/10.1016/j.fri.2021.200449. (https://www.sciencedirect.com/science/article/pii/S2666225621000208)

2. Denoising:

Beata Wereszczyńska, k-space weighting and masking for denoising of MRI image without blurring or losing contrast, as well as for brightening of the objects in the image with simultaneous noise reduction, 2022, https://doi.org/10.5281/zenodo.7367057. (https://github.com/BeataWereszczynska/k-space_wght_msk_for_MRI_denoising)

3. Detail control:

Beata Wereszczyńska, Graduate k-space masking for MRI image denoising and blurring, 2022, https://doi.org/10.5281/zenodo.7359195. (https://github.com/BeataWereszczynska/k-space_masking_for_MRI_denoising)

License

The software is licensed under the MIT license. The non-software content of this project is licensed under the Creative Commons Attribution 4.0 International license. See the LICENSE file for license rights and limitations.

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