DiplayRes.m

function Res = DiplayRes(Res,background); 	
% DISPLAYCI displays the results of the Pixel and Cluster tests.
%	tCi = DisplayCi(Res,[background]) returns, for the Pixel test 
% 	(typeTest = 'P'), the Z-scores (tCi) above threshold (tP); and, for the Cluster test 
% 	(typeTest = 'C'), the Z-scores (tCi) above threshold (tC) and belonging to clusters 
% 	greater than the minimum size (k).
% 
%		Res.ZCi	= Z-transformed smooth classification image (see ZTRANSCI) 
% 		Res.tC = Cluster test threshold (set by the user)
% 		Res.k = Minimun cluster size (see STAT_THRESHOLD)
% 		Res.tP = Pixel test threshold (see STAT_THRESHOLD)
% 		Res.FWHM = full width half maximum of the Gaussian smoothing filter (see  HALFMAX)
% 		Res.p = p-value for Pixel and Cluster tests (set by user)
% 		[background] = image overlaid to the classification image to help interpretation.
% 			With red pixels indicating the regions that attained statistical significance. 
% 
% 			E.g.,
% 			SCi = double(imread('GenderCi.tiff'));  
% 			sCi = cIm2hrCi(SCi); 
% 			sigma_b = 20;	
% 			S_r = double(imread('faceMask.tif'));
% 			vecSCi = SCi(eq(S_r,0));
% 			ZSCi = ZTransSCi(SCi,[mean(vecSCi(:)),std(vecSCi(:))]);
% 			ZSCi = ZSCi.*S_r;
% 			P = .05;	
% 			tC = 2.7; 	
% 			Res = StatThresh(ZSCi,p,sigma_b,tC,S_r);
% 			background = double(imread('w1H.JPG'));  
% 			tCi = DiplayRes(Res,background); 	
% 
%	For both tests, DISPLAYCI also returns a table with all relevant statistics (tC, tP, 
% 	p, sizes of significant clusters, and so on).
%
% Requires the ImageProcessing Toolbox.
% 
% See also STATRESH, ZTRANSCI, DEMOSTAT4CI
% 
% The Stat4Ci toolbox is free (http://mapageweb.umontreal.ca/gosselif/stat4ci.html); if you use 
% it in your research, please, cite us:
%	Chauvin, A., Worsley, K. J., Schyns, P. G., Arguin, M. & Gosselin, F. (2005).  Accurate 
%	statistical tests for smooth classification images.
% 
% Alan Chauvin & FrŽdŽric Gosselin (frederic.gosselin@umontreal.ca), 01//03/2005



% ------------------------------------------------------------
% 1. displays cluster result    
cCi = ( Res.ZSCi >= Res.tC);

[LabelClC ,nbClusterC] = BWLABEL(cCi);    % labels all clusters in Res.ZSCi
if nbClusterC ~=0
    
    for ii = 1 : nbClusterC
        cm(ii) = length(find(LabelClC == ii));
	end;
	%dbstop at 65
    cm = [[1:nbClusterC];cm];
    if nbClusterC > 1
		%cm = sortrows(cm,2);       % sorts clusters per size
		cm = sortrows(cm',1)';       % sorts clusters per size
	end;
    ii = 0;c= 0;
    while ii < nbClusterC
        ii = ii + 1;
        if cm(2,ii) < Res.k
            LabelClC(find(LabelClC == cm(1,ii))) = 0;
		else
            c = c +1;
            maxZ(c) = max(max(Res.ZSCi(find(LabelClC == cm(1,ii)))));
            sizeZ(c) = cm(2,ii);
            [I,J]=find(LabelClC == cm(1,ii));
            yZ(c) = round((max(I)+min(I))/2);
            xZ(c) = round((max(J)+min(J))/2);
            LabelClC(find(LabelClC == cm(1,ii))) = 1;
		end;
	end;
    for i = 1 : c
        Results(i,2) = Res.tC;
        Results(i,3) = sizeZ(i);
        Results(i,4) = sizeZ(i)/Res.FWHM/Res.FWHM';
        Results(i,5) = maxZ(i);	
        Results(i,6) = xZ(i);		
        Results(i,7) = yZ(i);
		if i == c		
			Results(i,1) = 67; % ascii code for 'C'
		else
	        Results(i,1) = 32; % ascii code for 'space'
		end;
	end;
	if c, clear maxZ sizeZ yZ xZ;end;
		
		
	if c > 0
		Results = flipud(sortrows(Results,3));
			
		fprintf('\n');
		fprintf('\tt\t\tsize\tresels\tZmax\tx\ty\n');
		fprintf('\t-----------------------------------------\n');
		fprintf('%c\t[%.2f]\t%d\t\t%.2f\t%.2f\t%d\t%d\n',Results');
		
	else

		fprintf('\t-----------------------------------------\n');
		fprintf('\tno cluster reach significance\n')
		
	end;
else
	fprintf('\t-----------------------------------------\n');
	fprintf('\tno cluster reach significance\n')
end;

% --------------------------------------------------------
% 2. displays pixel results
pCi = ( Res.ZSCi >= Res.tP);
nbClusterP = ( sum(pCi(:)) >= 1 );
if nbClusterP
	fprintf('\t-----------------------------------------\n');
	fprintf('P\t%.2f\t-\t-\t-\t-\t-\n',Res.tP);
end;
fprintf('\n');
fprintf('p-value = [%.2f]\n',Res.p);
fprintf('FWHM  == [%.1f]\n',Res.FWHM);
fprintf('Minimum cluster size = %.1f\n',max(Res.k,0));


% --------------------------------------------------------
% 3. displays the figure

if nbClusterC == 0 & nbClusterP == 0;
	Res.tCi = zeros(Res>ZSci);
	return;
end;

figure
for ii = 1 : 2
	if ii == 1
		% 3.1 Pixel test
		t = Res.tP;
		LabelCl = ones(size(Res.ZSCi));
		Res.PixTest = (Res.ZSCi >= t) .* LabelCl;
		tCi = Res.PixTest;
		ss = sprintf('Pixel Test');
	else
		% 3.1 Cluster  test
		t = Res.tC;
		LabelCl = LabelClC;
		Res.ClusTest = (Res.ZSCi >= t) .* LabelCl;
		tCi = Res.ClusTest;
		ss = sprintf('Cluster Test');
	end;

	subplot(1,2,ii)	
	if ~exist('background')  
		
		image(tCi);axis image;axis off
		s = sprintf('t = %.2f, pval = %.3f',t,p);	title(s)
		
	else	% thresholded classification image is overlaid on an image

		background = 2*( ( background-min(background(:)) ) / ( max(background(:))-min(background(:)) ) ) - 1;
		
		for ii = 1 : 3
			colorIma(:,:,ii) = 2*background;  
		end;
	
		mmax = max(Res.ZSCi(:));
		mmin = min(Res.ZSCi(:));
		
		cIma = colorIma/2;     cIma(:,:,1) = colorIma(:,:,1)/2 + tCi;
		
		cIma = ( cIma-min(cIma(:)) ) / ( max(cIma(:))-min(cIma(:))+0.0001 );
		
		tCi = cIma;
		
		% figure,imshow(cIma);axis on
		imagesc(cIma); axis square;colormap(jet);axis off
		s = sprintf('%s, t = %.2f, pval = %.3f',ss, t,Res.p);	title(s)
		if ii == 1
			% 3.1 Pixel test
			Res.PixTest = cIma;
		else
			% 3.1 Cluster  test
			Res.ClusTest = cIma;
		end;
	
	end;
end;
clear cm