View source for Faq how to automate PCA analysis for multiple images

===Issue:===

How do I automate PCA analysis for multiple images?

===Possible Solutions:===

Because the version of IMGPCA provided in the standard PLS_Toolbox requires some inputs to operate, IMGPCA is not well suited to automated analysis. Although future versions of the PLS_Toolbox will allow this, the current version requires you use the basic PCA routine and handle the image aspects yourself. 

First, create a preprocessing structure use the preprocess function: 
 >> s = preprocess;

This will bring up a dialog box that lets you specify what preprocessing you want. When you click "OK" it will return a preprocessing structure, s. (Alternatively, you can request the preprocessing method directly using the 'default' keyword. See "preprocess help" for more information.)

 s = 
     description: 'Mean Center' 
       calibrate: {'[data,out{1}] = mncn(data);'} 
           apply: {'data = scale(data,out{1});'} 
            undo: {'data = rescale(data,out{1});'} 
             out: {} 
     settingsgui: '' 
   settingsonadd: 0 
     usesdataset: 0 
      caloutputs: 1 
         keyword: 'Mean Center' 
        userdata: [] 

Next, create a PCA options structure using: 
 >> opts = pca('options') 

 opts = 
            name: 'options' 
         display: 'on' 
           plots: 'final' 
   outputversion: 3 
   preprocessing: {[]} 
    blockdetails: 'standard' 

then put the preprocessing structure <code>s</code> into this: 
 >> opts.preprocessing{1} = s 

 opts = 
            name: 'options' 
         display: 'on' 
           plots: 'final' 
   outputversion: 3 
   preprocessing: {[1x1 struct]} 
    blockdetails: 'standard' 

Turn off the display, and turn the plots to 'none': 
 >> opts.display = 'off';
 >> opts.plots = 'none'; 
Take you data and reshape it to number of pixels by number of channels 
(3 in your case) using the <code>reshape</code> function. 
 >> data = reshape(data,size(data,1)*size(data,2),size(data,3)); 
Then use the PCA function like: 
 >> model = pca(data,2,opts); 
The loadings will be in the <code>model.loads</code> field.


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[[Category:FAQ]]