Introduction and Acknowledgements
This module offer a simple application ...
- Use Case 1: Noise reduction as a preprocessing step for tissue segmentation
- When dealing with single voxel classification schemes running noise reduction as a preprocessing scheme will reduce the number of single misclassified voxels.
- Use Case 2: Preprocessing to volume rendering
- Noise reduction will result in nicer looking volume renderings
- Use Case 3: Noise reduction as part of image processing pipeline
- Could offer a better segmentation and classification on specific brain image analysis such as in Multiple Sclerosis lesion segmentation
Panels and their use
- Input Volume
- Select the input image
- Output Volume
- Set the output image file which the filters should place the final result
- The conductance regulates the diffusion intensity in the neighborhood area. Choose a higher conductance if the input image has strong noise seem in the whole image space.
- Use Auto Conductance
- Choose if you want to use an automatic adjustment of conductance parameter. If this is checked, the inserted value is ignored and the optimization function below is used.
- Optimization Function
- A set of optimization function for automatic estimation of conductance parameter. This is helpful is you do not have an initial guess on what value is appropriate to the conductance setting. (Canny, MAD and Morphological). Please see the Insight-Journal article that explain each of these automatic conductance adjustment methods.
- Number of Iteractions
- The number of iterations regulates the numerical simulation of the anomalous process over the image. This parameters is also related with the de-noising intensity, however it is more sensible to the noise intensity. Choose the higher number of iterations if the image presents high intensity noise which is not well treated by the conductance parameter.
- Time Step
- The time step is a normalization parameters for the numerical simulation. The maximum value, given as default, is set to 3D images. Lower time step restrict the numerical simulation of the anomalous process.
- Anomalous parameter
- The anomalous parameter (or q value) is the generalization parameters responsible to give the anomalous process approach on the diffusion equation. See the reference paper to choose the appropriate q value (at moment, only tested in MRI T1 and T2 weighted images).
- Senra Filho, A.C. & Murta Junior, L. O., 2017. Automatic Conductance Estimation Methods for Anisotropic Diffusion ITK Filters. Insight-Journal. website: http://www.insight-journal.org/browse/publication/983
- da S Senra Filho, A.C., Garrido Salmon, C.E. & Murta Junior, L.O., 2015. Anomalous diffusion process applied to magnetic resonance image enhancement. Physics in Medicine and Biology, 60(6), pp.2355–2373. DOI: 10.1088/0031-9155/60/6/2355
- Filho, A.C. da S.S. et al., 2014. Anisotropic Anomalous Diffusion Filtering Applied to Relaxation Time Estimation in Magnetic Resonance Imaging. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp. 3893–3896.
- Filho, A.C. da S.S., Barizon, G.C. & Junior, L.O.M., 2014. Myocardium Segmentation Improvement with Anisotropic Anomalous Diffusion Filter Applied to Cardiac Magnetic Resonance Imaging. In Annual Meeting of Computing in Cardiology.
Information for Developers
|Section under construction.|
- Da S Senra Filho, A. C., Garrido Salmon, C. E., & Murta Junior, L. O. (2015). Anomalous diffusion process applied to magnetic resonance image enhancement. Physics in Medicine and Biology, 60(6), 2355–2373. doi:10.1088/0031-9155/60/6/2355