Documentation/Nightly/Modules/IADImageFilter

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Home < Documentation < Nightly < Modules < IADImageFilter

For the stable Slicer documentation, visit the 4.10 page.

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  

Module Description

This module offer a simple application to the Isotropic Anomalous Diffusion (IAD) filter, which is able to increase the image SNR and preserve fine object's details around the image space. This method was studied on MRI structural images (T1 and T2), which other imaging modalities could be properly investigated in the future.

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

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:

  • Generalized Diffusion
    • The generalized diffusion regulates the diffusion intensity by setting the diffusion coefficient in the PDE iterative algorithm. Choose a higher generalized diffusion if the input image has strong noise seen in the entire image space.
  • Number of Iterations
    • The number of iterations regulates the numerical simulation of the anomalous process over the image. This parameter 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 parameter for the numerical simulation. The maximum value, given by default, is set to 3D images. Lower time step restricts 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[1] to choose the appropriate q value (at moment, only tested in MRI T1 and T2 weighted images).


Similar Modules

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. Brain Activation Inhomogeneity Highlighted by the Isotropic Anomalous Diffusion Filter. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago: IEEE, pp. 3313–3316.
  • Senra Filho, A.C. da S., Duque, J.J. & Murta, L.O., 2013. Isotropic anomalous filtering in Diffusion-Weighted Magnetic Resonance Imaging. I. E. in M. and B. Society, ed. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2013, pp.4022–5.

Information for Developers

  1. 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