mfbox_pre_roiselect_run.m

toolbox for spm 5 for data, model free analysis

function [X,mask,grid,timeline,params,private]=mfbox_pre_roiselect_run(X,mask,grid,timeline,params,runflag,private)
% select voxels using a brainmaskfile
%
% Usage:
%  [X,mask,grid,timeline,params,private]=mfbox_pre_roiselect_run(X,mask,grid,timeline,params,runflag,private)
%
%  X        - (NxT) data
%             or struct for mfbox_databackend('getdata',X,i),[],1);
%  mask     - data mask (XxYxZ) with sum(mask(:))==N
%  grid     - 3d positions (3xN) of the data values
%  timeline - timeline (1xT)
%  params   - struct with
%             priority   - evaluation priority
%             mask       - explicit mask
%             threshold  - threshold
%             brainmask  - brainmask
%             bmfile     - bmfile
%             manualmask - manualmask
%  runflag  - -1 get default parameter
%              0 interactive ask parameters
%              1 interactive ask parameters and run
%              2 run
%  private  - private data to enable plot updates while selecting (see also mfbox_databackend)
%
% Copyright by Peter Gruber, Fabian J. Theis, Ingo R. Keck
% Signal Processing & Information Theory group
% Institute of Biophysics, University of Regensburg, Germany
% Homepage: http://research.fabian.theis.name
%           http://www-aglang.uni-regensburg.de
%
% This file is free software, subject to the 
% GNU GENERAL PUBLIC LICENSE, see gpl.txt

error(nargchk(1,7,nargin));
error(nargchk(1,6,nargout));

if (isstruct(X)), s = [X.dim,X.timesteps];
else, s = size(X);
end
dim = s(1:(end-1));
if (length(dim)<2), dim = [1,dim]; end
n = s(end);

if (nargin<2), mask = true(dim); end
dim = size(mask);
if (length(dim)==length(s)), n = 1; end

if (nargin<3), grid = mfbox_mkgrid(dim)'; end
if (nargin<4), timeline = 0:(n-1); end
if (nargin<5), params = []; end
if (nargin<6), runflag = 1; end
if (nargin<7), private = []; end

params = mfbox_checkparam(params,'pre','roiselect', ...
    struct('mask',true(dim),'threshold',0.75, ...
    'priority',10,'brainmask',1,'bmfile','','manualmask',[]));

if (abs(runflag-0.5)<1)
    if (exist('OCTAVE_HOME')~=5)% matlab
        [params,private] = mfbox_pre_roiselectg(X,mask,grid,timeline,params,runflag,private);
    elseif (exist('OCTAVE_HOME')==5) %octave
        [params,runflag] = mfbox_getparam(params,runflag,'mfbox_pre_roiselectg.py');
    end
end

if (runflag>0 && isstruct(params))
    if (runflag<3)
        prgs = mfbox_progress([],'title','ROI','string','Registering brainmask...','progress',[1,4]);
    end

    nmask = true(dim);

    if (isfield(params,'manualmask'))
        if (~isempty(params.manualmask)), nmask = nmask & (params.manualmask>0); end
    end
    try
        if (params.brainmask==2)
            %if (isfield(handles,'private'))
            if (~isempty(private))
                [m,private] = mfbox_databackend('getbrainmask',private);
                m = reshape(m,dim); % the databackend will give us the mask as row vector
            else
                m = mfbox_databackend('getbrainmask');
                g = 1+(diag((size(m)-1)./(dim-1))*(mfbox_mkgrid(dim)'-1));
                g = mat2cell(g,ones(1,size(g,1)),size(g,2));
                m = reshape(interpn(m,g{:}),dim);
            end
            nmask = nmask&(m>params.threshold);
        elseif (params.brainmask==3)
            if (~isempty(private))
                [m,f,private] = mfbox_databackend('import_mask',params.bmfile,private);
                 m=reshape(m,dim); % the databackend will give us the mask as row vector
            else
                m = mfbox_databackend('import_mask',params.bmfile);
                g = 1+(diag((size(m)-1)./(dim-1))*(mfbox_mkgrid(dim)'-1));
                g = mat2cell(g,ones(1,size(g,1)),size(g,2));
                m = reshape(interpn(m,g{:}),dim);
            end
            nmask = nmask&(m>params.threshold);
        end
    catch
        v = lasterror;
        warning(['Something bad happened while roiselect' v.message]);
    end
    mask = mask & nmask;

    if (~isstruct(X))
        sgr = size(grid);
        sm = size(mask);
        g = mat2cell(min(repmat(sm',1,sgr(2)),max(ones(sgr), ...
            round(grid))),ones(1,sgr(1)),sgr(2));
        nmask = mask(sub2ind(sm,g{:}));
        grid = grid(:,nmask);
        X = reshape(X,[],n);
        X = X(nmask,:);
    end

    if (runflag<3), mfbox_progress(prgs,'close',[]); end
end