Difference between revisions of "Support:Documents:Examples:K-Means and Fuzzy C-Means analysis"

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1.Image >Clustering
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1.Image > Clustering
  
 
[[Image:K_Fuzzy_Means_1.png]]
 
[[Image:K_Fuzzy_Means_1.png]]
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[[Image:K_Fuzzy_Means_5.png]]
 
[[Image:K_Fuzzy_Means_5.png]]
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==Examples==
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Here  we use Fuzzy C-means for an example.
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1.Load phantom imge:
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<pre>
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a= phantom(256);
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b= phantom(256);
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c= cat(3,a,b);          % COMKAT do not read 2-D image
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comkatimagetool(c)
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</pre>
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2.Colormap > GreenFire
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[[Image:K_Fuzzy_Means_6.png]]
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3.Image > Clustering
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[[Image:K_Fuzzy_Means_7.png]]
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4.Set as follows:
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[[Image:K_Fuzzy_Means_8.png]]
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5.Results
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[[Image:K_Fuzzy_Means_9.png]]

Revision as of 17:02, 3 August 2015

K-Means and Fuzzy C-Means

In COMKAT R4.0a, you can analyze images by K-Means or Fuzzy C-Means.


1.Image > Clustering

K Fuzzy Means 1.png


2.You can select K-Means or Fuzzy C-Means here:

K Fuzzy Means 2.png


3.If you select Fuzzy C-means, you can choose the output Labels or Probability Maps /membership function :

K Fuzzy Means 3.png


4.Number of clusters

K Fuzzy Means 4.png


5.If your images are several MR contrast (muti-bands), you can also analyze a particular band alone by unchecking the checkbox as follows:

K Fuzzy Means 5.png


Examples

Here we use Fuzzy C-means for an example.


1.Load phantom imge: 

a= phantom(256);
b= phantom(256);
c= cat(3,a,b);           % COMKAT do not read 2-D image
comkatimagetool(c)

2.Colormap > GreenFire

K Fuzzy Means 6.png


3.Image > Clustering

K Fuzzy Means 7.png


4.Set as follows:

K Fuzzy Means 8.png

5.Results

K Fuzzy Means 9.png

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