Filters (Spatial): Nonmaximum Suppression and Filters (Spatial): Regularized Isotropic (Nonlinear) Diffusion: Difference between pages

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This algorithm defines the edges of an image by calculating the nonmaximum suppression of an image at a user-defined scale. An ''edge'' is defined as the union of points for which the gradient magnitude assumes a maximum in the gradient direction.
Regularized isotropic nonlinear diffusion is a specific technique within the general classification of diffusion filtering. Diffusion filtering, which models the diffusion process, is an iterative approach of spatial filtering in which image intensities in a neighborhood are utilized to compute new intensity values.


{| width="90%" border="1" frame="hsides" frame="hsides"
== Background ==
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Two major advantages of diffusion filtering over many other spatial domain filtering algorithms are:
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[[Image:noteicon.gif]]
* ''A priori'' image information can be incorporated into the filtering process;
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* The iterative nature of diffusion filtering allows for fine-grain control over the amount of filtering performed.
[http://mipav.cit.nih.gov/documentation/HTML Algorithms/FiltersSpatialNonmaximumSuppression.html For more information about the algorithm, refer to the MIPAV web site: {http://mipav.cit.nih.gov/documentation/HTML Algorithms/FiltersSpatialNonmaximumSuppression.html}. ]
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<br />
There is not a consistent naming convention in the literature to identify different types of diffusion filters. This documentation follows the approach used by  Weickert [http://www.mia.uni-saarland.de/weickert/demos.html]. Specifically, since the diffusion process relates a concentration gradient with a flux, ''isotropic diffusion'' means that these quantities are parallel. ''Regularized'' means that the image is filtered prior to computing the derivatives required during the diffusion process. In linear diffusion the filter coefficients remain constant throughout the image, while ''nonlinear'' diffusion means the filter coefficients change in response to differential structures within the image.


== Image types ==
== Image types ==


The nonmaximum suppression image can be generated from 2D, 2.5D, and 3D black-and-white images. However, the option to output an edge image is only available for black-and-white 2D images.
You can apply this algorithm to all data types except complex and to 2D, 2.5D, and 3D images.


== Applying the Nonmaximum Suppression algorithm ==
== Applying Regularized Isotropic (Nonlinear) Diffusion ==


To run this algorithm, complete the following steps:
To run this algorithm, complete the following steps:


# Open an image.
# Select Algorithms &gt; Filter &gt; Regularized Isotropic Diffusion. The Regularized Isotropic Diffusion dialog box opens (Figure 1).
# Perform, as an option, any other image processing, such as improving the contrast, on the image.
# Complete the fields in the dialog box.
# Select Algorithms &gt; Filter (spatial) &gt; Nonmaximum suppression. The Nonmaximum suppression dialog box (Figure 1) opens.
# When complete, click OK.
# 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: "Calculating the Nonmaximum suppression."
; 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.
 
{| width="90%" border="1" frame="hsides" frame="hsides"
|-
| width="9%" valign="top" |
[[Image:noteicon.gif]]
| width="81%" bgcolor="#B0E0E6" | '''Note:''' For 2D images, if you selected Output edge image, a new window with an edge image appears.
|}


<br /><div>
; The algorithm begins to run, and a status window appears. When the algorithm finishes, the resulting image appears in a new image window.


== Nonmaximum Suppression dialog box ==
<div><br /> </div><div>


{| border="1" cellpadding="5"
{| border="1" cellpadding="5"
|+ <div>'''Figure 1. Nonmaximum Suppression dialog box ''' </div>
|+ <div>'''Figure 1. Regularized Isotropic Diffusion dialog box ''' </div>
|-
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<div>'''X dimension''' </div>
<div>'''Number of iterations''' </div>
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<div>Indicates the scale of the Gaussian in the ''X'' direction (the default value is 1.0). </div>
<div>Specifies the number of iterations, or number of times, to apply the algorithm to the image. </div>
| rowspan="4" colspan="1" |
| rowspan="4" colspan="1" |
<div><div><center>[[Image:dialogboxNonmaximumSuppression.jpg]]</center></div> </div><div> </div><div> </div><div> </div><div> </div><div> </div><div> </div><div> </div><div> </div><div> </div><div> </div><div> </div><div> </div><div> </div>
<div><div><center>[[Image:dialogboxRegularizedIsotropicDiffusion.jpg]]</center></div> </div>
|-
|-
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<div>'''Y dimension''' </div>
<div>'''Gaussian standard deviation''' </div>
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<div>Indicates the scale of the Gaussian in the ''Y'' direction (the default value is 1.0). </div>
<div>Specifies the standard deviation of the Gaussian filter used to regularize the image. </div>
|-
|-
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<div>'''Z dimension''' </div>
<div>'''Diffusion contrast parameter''' </div>
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<div>Indicates the scale of the Gaussian in the ''Z'' direction (for 3D images only). The default value is 1.0. </div>
<div>Specifies the inflection point in the diffusivity function, which dictates the shape of the function. </div>
|-
|-
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<div>'''Use image resolutions to normalize ''''''Z'''''' scale''' </div>
<div>'''Process each slice separately''' </div>
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<div>Normalizes the ''Z'' scale to compensate for the difference if the voxel resolution in distance per pixel is greater between slices than the voxel resolution in-plane (for 3D images only, the default value is enabled).  </div><div>If enabled, then [[Image:FiltersSpatialNonmaximumSuppression3.jpg]] where ''Z'' = scale Z, ''XRs'' = image X resolution, and ''ZRs'' = image Z resolution).  </div>
|-
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<div>'''Process each slice independently''' </div>
<div>Applies the algorithm to each slice individually. By default, this option is selected. </div>
| rowspan="1" colspan="2" |
<div>Calculates nonmaximum suppression for each slice of the dataset independently (for 3D images only the default value is enabled). </div>
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<div>'''New image''' </div>
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<div>Shows the results of the algorithm in a new image window (default choice). If selected, an output edge image appears in a second new window. </div>
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<div>'''Replace image''' </div>
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<div>Replaces the current active image with the results of the algorithm. </div>
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<div>'''Whole image''' </div>
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<div>Applies the algorithm to the whole image (default choice). </div>
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<div>'''VOI region(s)''' </div>
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<div>Applies the algorithm inside VOIs. Outside VOIs, the pixel values are unchanged. </div>
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<div>'''OK''' </div>
<div>'''OK''' </div>
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<div>Applies the algorithm according to the specifications in this dialog box. </div>
<div>Applies the algorithm according to the specifications in this dialog box. </div>
|-
|-
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<div>'''Cancel''' </div>
<div>'''Cancel''' </div>
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<div>Disregards any changes that you made iin the dialog box and closes this dialog box. </div>
<div>Disregards any changes that you made in this dialog box and closes the dialog box. </div>
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|-
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<div>Displays online help for this dialog box. </div>
<div>Displays online help for this dialog box. </div>
|}
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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): Nonlinear Noise Reduction|Nonlinear Noise Reduction]]
**[[Filters (Spatial): Slice Averaging]]




[[Category:Help]]
[[Category:Help]]
[[Category:Help:Algorithms]]
[[Category:Help:Algorithms]]

Revision as of 18:44, 24 May 2013

Regularized isotropic nonlinear diffusion is a specific technique within the general classification of diffusion filtering. Diffusion filtering, which models the diffusion process, is an iterative approach of spatial filtering in which image intensities in a neighborhood are utilized to compute new intensity values.

Background

Two major advantages of diffusion filtering over many other spatial domain filtering algorithms are:

  • A priori image information can be incorporated into the filtering process;
  • The iterative nature of diffusion filtering allows for fine-grain control over the amount of filtering performed.

There is not a consistent naming convention in the literature to identify different types of diffusion filters. This documentation follows the approach used by Weickert [1]. Specifically, since the diffusion process relates a concentration gradient with a flux, isotropic diffusion means that these quantities are parallel. Regularized means that the image is filtered prior to computing the derivatives required during the diffusion process. In linear diffusion the filter coefficients remain constant throughout the image, while nonlinear diffusion means the filter coefficients change in response to differential structures within the image.

Image types

You can apply this algorithm to all data types except complex and to 2D, 2.5D, and 3D images.

Applying Regularized Isotropic (Nonlinear) Diffusion

To run this algorithm, complete the following steps:

  1. Select Algorithms > Filter > Regularized Isotropic Diffusion. The Regularized Isotropic Diffusion dialog box opens (Figure 1).
  2. Complete the fields in the dialog box.
  3. When complete, click OK.
The algorithm begins to run, and a status window appears. When the algorithm finishes, the resulting image appears in a new image window.

Figure 1. Regularized Isotropic Diffusion dialog boxÂ
Number of iterations
Specifies the number of iterations, or number of times, to apply the algorithm to the image.
DialogboxRegularizedIsotropicDiffusion.jpg
Gaussian standard deviation
Specifies the standard deviation of the Gaussian filter used to regularize the image.
Diffusion contrast parameter
Specifies the inflection point in the diffusivity function, which dictates the shape of the function.
Process each slice separately
Applies the algorithm to each slice individually. By default, this option is selected.
OK
Applies the algorithm according to the specifications in this dialog box.
Cancel
Disregards any changes that you made in this dialog box and closes the dialog box.
Help
Displays online help for this dialog box.

See also:

Retrieved from "https://mipav.cit.nih.gov/pubwiki/index.php?title=Filters_(Spatial):_Nonmaximum_Suppression&oldid=209"