imgpca2.m

GUI for Multivariate Image Analysis of 4-dimensional data (Matlab Code)

function model = imgpca2(mwa,scaling,nocomp)
%IMGPCA Principal Components Analysis of Multivariate Images.
%  IMGPCA uses principal components analysis to make
%  psuedocolor maps of multivariate images. The input is the
%  multivariate image (mwa). Optional inputs are (scaling) the
%  scaling to be used, and the number of PCs to calculate (nocomp).
%    scaling = 'auto' uses autoscaling {default},
%    scaling = 'mncn' uses mean centering, and
%    scaling = 'none' uses no scaling.
%
%  It is assumed that the image (mwa) is a 3 dimensional (m x n x p)
%  array where each image is m x n pixels and there are p images.
%  IMGPCA presents each scores, residual, and T^2 matrix as a
%  psuedocolor image. If 3 are more PCs are selected (nocomp>=3),
%  a composite of the first three  PCs is shown as an rgb image,
%  with red for the first PC, green for the second, and blue for the
%  the third.
%
%  The output (model) is a structure with the following fields:
%
%     xname: input data name
%      name: type of model, always 'IPCA'
%      date: date of model creation
%      time: time of model creation
%      size: dimensions of input data
%    nocomp: number of PCs in model
%     scale: type of scaling used
%     means: mean vector for PCA model
%      stds: standard deviation vector for PCA model
%       ssq: variance captured table data
%    scores: PCA scores stored as m x n x nocomp array (uint8)
%     range: original range of PCA scores before mapping to uint8
%     loads: PCA loadings
%       res: PCA residuals stored as m x n array (uint8)
%    reslim: Q limit
%       tsq: PCA T^2 values stared as m x n array (unit8)
%    tsqlim: T^2 limit
%
%  Note that the scores, residuals and T^2 matrices are stored
%  as unsigned 8 bit integers (uint8) scaled so their range is 
%  0 to 255. These can be viewed with the IMAGE function, but 
%  be sure the current colormap has 256 colors. For example, to
%  view the scores on the second PC using the jet colormap:
%
%   image(model.scores(:,:,2)), colormap(jet(256)), colorbar
%
%I/O: model = imgpca(mwa,scaling,nocomp);
% 
% IMGPCA can also be used to apply existing IPCA models to 
% new images as follows:
%
%I/O: newmod = imgpca(mwa,model,plots);
%
% If plots == 0, no plots are produced.
%
%See also: CONTRASTMOD, IMAGEGUI, IMGSELCT, IMGSIMCA, IMREAD, ISIMCAPR

%Copyright Eigenvector Research, Inc. 1998-2004
%Licensee shall not re-compile, translate or convert "M-files" contained
% in PLS_Toolbox for use with any software other than MATLAB