Filters (Spatial) Laplacian and Filters (Wavelet): De-noising BLS GSM: Difference between pages

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''Edge detection'' is the identification of meaningful discontinuities in gray level or color images. Edges are formed between two regions that have differing intensity values. This algorithm calculates the laplacian of an image (or VOI of the image) using the second derivatives (''Gxx'', ''Gyy'', and ''Gzz'' [3D]) of the Gaussian function at a user-defined scale sigma [standard deviation (SD)] and convolving it with image. The convolution of the second derivatives of the Gaussian with an image is a robust method of extracting edge information. By varying the SD, a scale-space of edges can easily be constructed.
This page is a stub.


=== Background ===
== Background ==
The wavelet transform or wavelet analysis is a solution to overcome the shortcomings of the Fourier transform. In wavelet analysis, the modulated window is shifted along the signal, and for every position, the spectrum is calculated. Then, this process is repeated many times with a slightly shorter (or longer) window for every new cycle. In the end, the result appears as a collection of time-frequency representations of the signal, all with different resolutions. Since the modulated window is fully scalable, this solves the signal-cutting problem which arises in the Fourier transform.


Figure 1 shows the 1D signal of an object. The first derivative of the object is shown next. Last, the Laplacian (defined later) is shown. Note that the zero-crossing of the second derivative corresponds to the edges of the object.
Because the result appears as a collection of time-frequency representations of the signal, we can speak of a multi-resolution analysis. However, in the case of wavelets, we normally do not speak about time-frequency representations, but about time-scale representations, scale being in a way the opposite of frequency, because the term frequency is reserved for the Fourier transform.  


<div>
== Image types ==


{| border="1" cellpadding="5"
This algorithm only applies to 2D black and white images.
|+ <div>'''Figure 1. Edge detection using laplacian operators''' </div>
|-
|
<div><div align="left">[[Image:LaplacianGrid5.jpg]]</div> </div>
|}


</div>
== Applying the Wavelet BLS GSM algorithm ==


The laplacian function is defined as:''''
* Open an image of interest.
* Navigate to Algorithms > Filters (wavelet) > Denoising BLS GSM.
* The Denoising Bayesian Least Squares Gray Scale Mixture dialog box appears.
* Complete the dialog box. For more information about the dialog box options, refer to Denoising Bayesian Least  Squares Gray Scale Mixture dialog box.
* Specify where you wish the de-noised image to appear - in a new image frame or in the same frame replacing the existing image.
* Press OK.
* The algorithm begins to run and the progress window appears with the status. When the algorithm finishes running, the de-noised image appears in the designated image frame.


<math>
\triangledown^2G(x,y) = \frac {\partial^2G}{\partial x^2} + \frac {\partial^2G}{\partial y^2}
</math>


An efficient method of calculating the laplacian is to analytically calculate the second derivatives of Gaussian and convolve the sum with the image. The equation for this calculation follows:
==  Wavelet Thresholding dialog box ==


<div align="left">[[Image:FiltersSpatialLaplacian5.jpg]]</div> <div>
'''Denoising Bayesian Least Squares Gray Scale Mixture'''


{| border="1" cellpadding="5"
'''Orthogonal wavelet''' - this is very fast technique, but of relatively poor denoising performance, because it is not translation invariant. For the Orthogonal wavelet technique the following filter options available:
|+ <div>'''Figure 2. (A) Original MR image; (B) laplacian results; and (C) extraction of the zero crossing of the laplacian (object edges)''' </div>
|-
|
<div><div><center>[[Image:exampleLaplacianProcessing.jpg]]</center></div> </div>
|}


</div><div><br /> </div>
* Daubechies 2,3,4
* Haar
* Quadrative Mirror Filter 5, 8, 9, 12, 13, 16


==== Image types ====
Numbers after the filter name refer to a highest number A of vanishing moments for given support width N=2A used in the filter.
 
You can apply this algorithm to all image data types except complex and to 2D, 2.5D, and 3D images.
 
==== Special notes ====
 
The resulting image is, by default, a float type image.
 
To achieve 2.5D blurring (each slice of the volume is processed independently) of a 3D dataset, select Process each slice independently (2.5D) in the Laplacian dialog box (Figure 3).
 
==== References ====
 
Refer to the following references for more information about this algorithm:
 
Tony Lindeberg, "Linear Scale-Space I: Basic Theory," ''Geometry-Driven Diffusion in Computer Vision'', Bart M. Ter Har Romeney, ed. (Dordrecht, The Netherlands: Kluwer Academic Publishers, 1994), pp. 1-38.
 
J. J. Koenderink, "The Structure of Images," ''Biol Cybern'' 50:363-370, 1984.
 
Raphael C. Gonzalez and Richard E. Woods, ''Digital Image Processing'' (Boston: Addison-Wesley, 1992).
 
=== Applying the Laplacian algorithm ===
 
To run this algorithm, complete the following steps:
 
# Select Algorithms &gt; Filter &gt; Laplacian. The Laplacian dialog box opens (Figure 3).
# Complete the fields in the dialog box.
# When complete, click OK. The algorithm begins to run.
 
; A pop-up window appears with the status. The following message appears: "Calculating the Laplacian."
; When the algorithm finishes, the pop-up window closes. Depending on whether you selected New Image or Replace Image, the results either appear in a new window or replace the image to which the algorithm was applied.
 
<div>.
 
{| border="1" cellpadding="5"
|+ <div>'''Figure 3. Laplacian dialog box  ''' </div>
|-
|
<div>'''X Dimension''' </div>
|
<div>Specifies the standard deviation (SD) of Gaussian in the ''X'' direction. </div>
| rowspan="6" colspan="1" |
<div><div><center>[[Image:dialogboxLaplacian.jpg]]</center></div> </div><div><br /> </div><div><br /> </div>
|-
|
<div>'''Y Dimension''' </div>
|
<div>Specifies the SD of Gaussian in the ''Y'' direction. </div>
|-
|
<div>'''Z Dimension''' </div>
|
<div>Specifies the SD of Gaussian in the ''Z'' direction. </div>
|-
|
<div>'''Amplification factor''' </div>
|
<div>Applies, by default, the classical laplacian factor of 1.0; values greater than 1.0 and less than 1.2 enable the laplacian to act as a high-pass, or high-boost, filter. </div>
|-
|
<div>'''Use image resolutions to normalize Z scale''' </div>
|
<div>Normalizes the Gaussian to compensate for the difference if the voxel resolution is less than the voxel resolution inplane. </div>
|-
|
<div>'''Process each edge independently (2.5D)''' </div>
|
<div>Blurs each slice of the dataset independently. </div>
|-
|
<div>'''Output edge image''' </div>
| rowspan="1" colspan="2" |
<div>Produces a binary image that represents the edges defined by the 0 crossings of the laplacian </div>
|-
|
<div>'''Threshold edge noise between &lt;_&gt; and &lt;_&gt;''' </div>
| rowspan="1" colspan="2" |
<div>Limits the threshold edge noise to the range that you specify between the 0 crossings of the laplacian; the default range is -10 to 10.  </div>
|-
|
<div>'''New image''' </div>
| rowspan="1" colspan="2" |
<div>Shows the results of the algorithm in a new image window. </div>
|-
|
<div>'''Replace image''' </div>
| rowspan="1" colspan="2" |
<div>Replaces the current active image with the results of the algorithm. </div>
|-
|
<div>'''Whole image''' </div>
| rowspan="1" colspan="2" |
<div>Applies the algorithm to the whole image. </div>
|-
|
<div>'''VOI region(s)''' </div>
| rowspan="1" colspan="2" |
<div>Applies the algorithm to the volumes (regions) delineated by the VOIs. </div>
|-
|
<div>'''OK''' </div>
| rowspan="1" colspan="2" |
<div>Applies the laplacian algorithm according to the specifications in this dialog box.  </div>
|-
|
<div>'''Cancel''' </div>
| rowspan="1" colspan="2" |
<div>Disregards any changes that you made in this dialog box and closes the dialog box. </div>
|-
|
<div>'''Help''' </div>
| rowspan="1" colspan="2" |
<div>Displays online help for this dialog box. </div>
|}
 
== See also: ==
 
**[[Fast Fourier Transformation (FFT)]]
**[[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): Nonlinear Noise Reduction]]
**[[Filters (Spatial): Nonmaximum Suppression]]
**[[Filters (Spatial): Regularized Isotropic (Nonlinear) Diffusion]]
**[[Filters (Spatial): Slice Averaging]]
**[[Filters (Spatial): Unsharp Mask]]


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

Revision as of 15:19, 25 April 2013

This page is a stub.

Background

The wavelet transform or wavelet analysis is a solution to overcome the shortcomings of the Fourier transform. In wavelet analysis, the modulated window is shifted along the signal, and for every position, the spectrum is calculated. Then, this process is repeated many times with a slightly shorter (or longer) window for every new cycle. In the end, the result appears as a collection of time-frequency representations of the signal, all with different resolutions. Since the modulated window is fully scalable, this solves the signal-cutting problem which arises in the Fourier transform.

Because the result appears as a collection of time-frequency representations of the signal, we can speak of a multi-resolution analysis. However, in the case of wavelets, we normally do not speak about time-frequency representations, but about time-scale representations, scale being in a way the opposite of frequency, because the term frequency is reserved for the Fourier transform.

Image types

This algorithm only applies to 2D black and white images.

Applying the Wavelet BLS GSM algorithm

  • Open an image of interest.
  • Navigate to Algorithms > Filters (wavelet) > Denoising BLS GSM.
  • The Denoising Bayesian Least Squares Gray Scale Mixture dialog box appears.
  • Complete the dialog box. For more information about the dialog box options, refer to Denoising Bayesian Least Squares Gray Scale Mixture dialog box.
  • Specify where you wish the de-noised image to appear - in a new image frame or in the same frame replacing the existing image.
  • Press OK.
  • The algorithm begins to run and the progress window appears with the status. When the algorithm finishes running, the de-noised image appears in the designated image frame.


Wavelet Thresholding dialog box

Denoising Bayesian Least Squares Gray Scale Mixture

Orthogonal wavelet - this is very fast technique, but of relatively poor denoising performance, because it is not translation invariant. For the Orthogonal wavelet technique the following filter options available:

  • Daubechies 2,3,4
  • Haar
  • Quadrative Mirror Filter 5, 8, 9, 12, 13, 16

Numbers after the filter name refer to a highest number A of vanishing moments for given support width N=2A used in the filter.

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