Filters (Spatial): Nonlinear Noise Reduction
From MIPAV
Background
This algorithm provides a port of the SUSAN (Smallest Univalue Segment Assimilating Nucleus) low-level image processing program.
For information about SUSAN, see http://www.fmrib.ox.ac.uk/~steve.
SUSAN noise reduction uses nonlinear filtering to reduce noise in an image while preserving both the underlying structure and the edges and corners. It does this by averaging a voxel only with local voxels that have similar intensity.
Table 1 explains the possible responses that the SUSAN filter provides if it cannot determine the neighborhood and based on whether you selected the option of using the median.
Use median
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SUSAN area
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The SUSAN filter . . .
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Selected
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Zero
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Uses the median of the pixel's 8 nearest neighbors in 2D images or 26 nearest neighbors in 3D images to estimate the pixel's correct value.
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Selected
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Nonzero
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Uses sums taken over the local neighborhood, not including the center pixel itself, to estimate the pixel's correct value. This allows good reduction of impulse noise.
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Not selected
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Nonzero
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Uses sums taken over the local neighborhood including the center pixel to estimate the pixel's correct value.
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Not selected
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Zero
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Leaves the pixel unchanged.
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Image types
You can apply this algorithm to black-and-white 2D, 2.5D, and 3D images.
Special notes
None.
Applying the algorithm
To run this algorithm, complete the following steps:
- Open an image.
- Select Algorithms > Filter (spatial) > Nonlinear noise reduction. The Nonlinear Noise Reduction dialog box (Figure 1) opens.
- Complete the information in the dialog box.
- Click OK. The algorithm begins to run.
- A pop-up window appears with the status. The following message appears
- "Performing the nonlinear noise reduction." When the algorithm finishes running, the pop-up window closes.
- Depending on whether you selected New image or Replace image, the results appear in a new window or replace the image to which the algorithm was applied.
Brightness threshold
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Blurs edges of contrast smaller than this threshold. Reducing the brightness threshold gives less smoothing. The default value is 0.1 * (image maximum - image minimum).
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Mask Gaussian SD (0 for flat response) mm
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Determines the spatial extent of the smoothing (the default value is equal to the X resolution). For a small, fast, flat response with a 3 x 3 or 3 x 3 x 3 voxel mask, set the mask SD to 0.
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Use median when neighborhood not found
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By default, this check box is selected.
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Process each slice independently
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Smooths each slice of the dataset independently (applies only to 3D images).
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New image
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Shows the results of the algorithm in a new image window (default choice).
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Replace image
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Replaces the current active image with the newly calculated image.
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OK
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Applies the algorithm according to the specifications in this dialog box.
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Cancel
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Disregards any changes that you made in the dialog box and closes the dialog box.
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Help
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Displays online help for this dialog box.
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See also:
- Filters (Spatial): Adaptive Noise Reduction
- Filters (Frequency)
- Filters (Spatial): Adaptive Path Smooth
- Filters (Spatial) Anisotropic Diffusion
- Filters (Spatial): Coherence-Enhancing Diffusion
- Filters (Spatial): Gaussian Blur
- Filters (Spatial): Gradient Magnitude
- Filters (Spatial): Haralick Texture
- Filters (Spatial) Laplacian
- Filters (Spatial): Local Normalization
- Filters (Spatial): Mean
- Filters (Spatial): Median
- Filters (Spatial): Mode
- Filters (Spatial): Nonmaximum Suppression
- Filters (Spatial): Regularized Isotropic (Nonlinear) Diffusion
- Filters (Spatial): Slice Averaging
- Filters (Spatial): Unsharp Mask