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limo_findcluster.m
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limo_findcluster.m
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function [cluster, num] = limo_findcluster(onoff, spatdimneighbstructmat, varargin)
% limo_findcluster returns all connected clusters in a 3 dimensional matrix
% with a connectivity of 6.
%
% FORMAT: [cluster, num] = limo_findcluster(onoff, spatdimneighbstructmat)
% [cluster, num] = limo_findcluster(onoff, spatdimneighbstructmat, minnbchan)
% [cluster, num] = limo_findcluster(onoff, spatdimneighbstructmat, spatdimneighbselmat, minnbchan)
%
% INPUT: onoff is a 3D boolean matrix with size N1xN2xN3 (typically T/F values > threshold)
% spatdimneighbstructmat defines the neighbouring channels/combinations, see below
% spatdimneighbselmat is a special neighbourhood matrix that is used for selecting
% channels/combinations on the basis of the minnbchan criterium
% minnbchan the minimum number of neighbouring channels/combinations
%
% The neighbourhood structure for the first dimension is specified using
% spatdimneighbstructmat, which is a 2D (N1xN1) matrix. Each row and each column corresponds
% to a channel (combination) along the first dimension and along that row/column, elements
% with "1" define the neighbouring channel(s) (combinations). The first dimension of
% onoff should correspond to the channel(s) (combinations). The lower triangle of
% spatdimneighbstructmat, including the diagonal, is assumed to be zero.
%
% OUTPUT: cluster is the map of clusters with their respective label
% num is the number of cluster
%
% This file is taken from FieldTrip,
% see <http://www.ru.nl/neuroimaging/fieldtrip> for the documentation and details.
%
% FieldTrip is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
% % Copyright (C) 2004, Robert Oostenveld
%
% $Id: findcluster.m 952 2010-04-21 18:29:51Z roboos $
% reused as limo_ft_findluster for integration in LIMO toolbox: GAR, University of Glasgow, June 2010
% Edit Marianne Latinus adding spm_bwlabel, June 2013
% limo_findcluster is a refactoring of limo_ft_findcluster with the same functionality
% and some optimisations for speed. spm_bwlabel is now used preferentially if availiable
% calls to ismember rearranged and replaced with ismembc
%
% Andrew Stewart, August 2014
% --------------------------------------
% Copyright (C) LIMO Team 2016
spatdimlength = size(onoff, 1);
nfreq = size(onoff, 2);
ntime = size(onoff, 3);
if length(size(spatdimneighbstructmat))~=2 || ~all(size(spatdimneighbstructmat)==spatdimlength)
error('invalid dimension of spatdimneighbstructmat');
end
minnbchan=2;
if length(varargin)==1
minnbchan=varargin{1};
end;
if length(varargin)==2
spatdimneighbselmat=varargin{1};
minnbchan=varargin{2};
end;
if minnbchan>0
% For every (time,frequency)-element, it is calculated how many significant
% neighbours this channel has. If a significant channel has less than minnbchan
% significant neighbours, then this channel is removed from onoff.
if length(varargin)==1
selectmat = single(spatdimneighbstructmat | spatdimneighbstructmat');
end;
if length(varargin)==2
selectmat = single(spatdimneighbselmat | spatdimneighbselmat');
end;
nremoved=1;
while nremoved>0
nsigneighb=reshape(selectmat*reshape(single(onoff),[spatdimlength (nfreq*ntime)]),[spatdimlength nfreq ntime]);
remove=(onoff.*nsigneighb)<minnbchan;
nremoved=length(find(remove.*onoff));
onoff(remove)=0;
end;
end;
% for each channel (combination), find the connected time-frequency clusters
labelmat = zeros(size(onoff));
total = 0;
% axs - Check for requisites once, rather than within loops
if exist('spm_bwlabel','file') == 3 % axs - preferentially use spm_bwlabel mex if it is in path
for spatdimlev=1:spatdimlength
[labelmat(spatdimlev, :, :), num] = spm_bwlabel(double(reshape(onoff(spatdimlev, :, :), nfreq, ntime)), 6);
labelmat(spatdimlev, :, :) = labelmat(spatdimlev, :, :) + (labelmat(spatdimlev, :, :)~=0)*total;
total = total + num;
end
elseif exist('bwlabeln','file') == 2 && exist('spm_bwlabel','file') ~= 3
for spatdimlev=1:spatdimlength
[labelmat(spatdimlev, :, :), num] = bwlabeln(reshape(onoff(spatdimlev, :, :), nfreq, ntime), 4);
labelmat(spatdimlev, :, :) = labelmat(spatdimlev, :, :) + (labelmat(spatdimlev, :, :)~=0)*total;
total = total + num;
end
else
errordlg('You need either the Image Processing Toolbox or SPM in your path to do clustering');
end
% combine the time and frequency dimension for simplicity
labelmat = reshape(labelmat, spatdimlength, nfreq*ntime);
% combine clusters that are connected in neighbouring channel(s)
% (combinations).
replaceby=1:total;
for spatdimlev=1:spatdimlength
neighbours=find(spatdimneighbstructmat(spatdimlev,:));
for nbindx=neighbours
indx = find((labelmat(spatdimlev,:)~=0) & (labelmat(nbindx,:)~=0));
for i=1:length(indx)
a = labelmat(spatdimlev, indx(i));
b = labelmat(nbindx, indx(i));
if replaceby(a)==replaceby(b)
% do nothing
continue;
elseif replaceby(a)<replaceby(b)
% replace all entries with content replaceby(b) by replaceby(a).
replaceby(find(replaceby==replaceby(b))) = replaceby(a);
elseif replaceby(b)<replaceby(a)
% replace all entries with content replaceby(a) by replaceby(b).
replaceby(find(replaceby==replaceby(a))) = replaceby(b);
end
end
end
end
% renumber all the clusters
num = 0;
cluster = zeros(size(labelmat));
uniquelabel=unique(replaceby(:))';
for j = 1:length(uniquelabel)
num = num+1;
uniquelabel_here = find(replaceby==uniquelabel(j));
%labelmat_is_unique_here = ismember(labelmat(:),uniquelabel_here);
labelmat_is_unique_here = ismembc(labelmat(:),uniquelabel_here); % axs - Using the semi-documented ismembc is x3 faster than ismember
% For ismembc to work, uniquelabel_here MUST be sorted
% low-high. This should be the case.
cluster(labelmat_is_unique_here) = num;
end
% reshape the output to the original format of the data
cluster = reshape(cluster, spatdimlength, nfreq, ntime);