Documentation/Nightly/Modules/AADImageFilter

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Introduction and Acknowledgements

Extension: AnomalousFilters Webpage: http://dcm.ffclrp.usp.br/csim/ Author: Antonio Carlos da S. Senra Filho, CSIM Laboratory (University of Sao Paulo, Department of Computing and Mathematics) Contact: Antonio Carlos da S. Senra Filho, <email>acsenrafilho@usp.br</email>

CSIM Laboratory   University of Sao Paulo   CAPES Brazil

Diffusion process is widely applied to digital image enhancement both directly introducing diffusion equation as in anisotropic diffusion (AD) filter, and indirectly by convolution as in Gaussian filter. Anomalous diffusion process (ADP), given by a nonlinear relationship in diffusion equation and characterized by an anomalous parameters q, is supposed to be consistent with inhomogeneous media.The ADP presented a more effective enhancement for images containing Rayleigh and Gaussian noise. Furthermore, the AAD and IAD filters showed optimum results for noise distributions that appear on extreme situations on MRI, i.e. in low SNR images with approximate Rayleigh noise distribution, and for high SNR images with Gaussian or non central χ noise distributions. AAD and IAD filter showed the best results for the parametric range 1.2 < q < 1.6, suggesting that the anomalous diffusion regime is more suitable for MRI.

Module Description

This module aims to ...

Use Cases

• 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

• 

  Raw T1 weighted MRI Image

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  T1 weighted MRI Image with AAD filter (q=1.2)

Tutorials

N/A

Panels and their use

User Interface

IO:

• Input Volume
  • Select the input image
• Output Volume
  • Set the output image file which the filters should place the final result

Diffusion Parameters:

• Conductance
  • The conductance regulates the diffusion intensity in the neighbourhood area. Choose a higher conductance if the input image has strong noise seem in the whole image space.
• 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).

Similar Modules

• IAD Image Filter
• Gradient Anisotropic Image Filter

References

• 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.