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| function x = spm_coreg(varargin)
if nargin>=4,
x = optfun(varargin{:});
return;
end;
def_flags = struct('sep',[4 2],'params',[0 0 0 0 0 0], 'cost_fun','nmi','fwhm',[7 7],...
'tol',[0.02 0.02 0.02 0.001 0.001 0.001 0.01 0.01 0.01 0.001 0.001 0.001],'graphics',1);
if nargin < 3,
flags = def_flags;
else
flags = varargin{3};
fnms = fieldnames(def_flags);
for i=1:length(fnms),
if ~isfield(flags,fnms{i}), flags.(fnms{i}) = def_flags.(fnms{i}); end;
end;
end;
%disp(flags)
if nargin < 1,
VG = spm_vol(spm_select(1,'image','Select reference image'));
else
VG = varargin{1};
if ischar(VG), VG = spm_vol(VG); end;
end;
if nargin < 2,
VF = spm_vol(spm_select(Inf,'image','Select moved image(s)'));
else
VF = varargin{2};
if ischar(VF) || iscellstr(VF), VF = spm_vol(strvcat(VF)); end;
end;
if ~isfield(VG, 'uint8'),%codage en format 8bit
VG.uint8 = loaduint8(VG);
vxg = sqrt(sum(VG.mat(1:3,1:3).^2));%calcul le volume des voxels
fwhmg = sqrt(max([1 1 1]*flags.sep(end)^2 - vxg.^2, [0 0 0]))./vxg;% distance entre voxels
VG = smooth_uint8(VG,fwhmg); % Note side effects
end;
sc = flags.tol(:)'; % Required accuracy
sc = sc(1:length(flags.params));
xi = diag(sc*20); %diagonalisation de xi (c'est matrice)
for k=1:numel(VF), % de 1 jusqu'au nombre d'éléments du vecteur volume de l'image fonctionnelle
VFk = VF(k);
if ~isfield(VFk, 'uint8'), %on vérifie si chaque fichier est ds le format de codage 8bit
VFk.uint8 = loaduint8(VFk);
vxf = sqrt(sum(VFk.mat(1:3,1:3).^2));
fwhmf = sqrt(max([1 1 1]*flags.sep(end)^2 - vxf.^2, [0 0 0]))./vxf;
VFk = smooth_uint8(VFk,fwhmf); % Note side effects
end;
xk = flags.params(:);%calcul du vecteur
for samp=flags.sep(:)',
xk = spm_powell(xk(:), xi,sc,mfilename,VG,VFk,samp,flags.cost_fun,flags.fwhm); %optimisation avec la méthode de Powell
x(k,:) = xk(:)';
end;
if flags.graphics,
display_results(VG(1),VFk(1),xk(:)',flags); %flags prend la valeur de xk(:)
end;
end;
return;
%_______________________________________________________________________
%_______________________________________________________________________
function o = optfun(x,VG,VF,s,cf,fwhm)
% The function that is minimised.
if nargin<6, fwhm = [7 7]; end; %si j'ai saisie que 5 param, le lissage à faire est de 256*256
if nargin<5, cf = 'mi'; end; %fonction cout:information mutuelle
if nargin<4, s = [1 1 1]; end; % ???
% Voxel sizes
vxg = sqrt(sum(VG.mat(1:3,1:3).^2));
sg = s./vxg; %est ce que tous les voxels sont un truc bidule
% Create the joint histogram
H = spm_hist2(VG.uint8,VF.uint8, VF.mat\spm_matrix(x(:)')*VG.mat ,sg);
% Smooth the histogram %lissage de l'histogramme
lim = ceil(2*fwhm);
krn1 = smoothing_kernel(fwhm(1),-lim(1):lim(1)) ; krn1 = krn1/sum(krn1); H = conv2(H,krn1);
krn2 = smoothing_kernel(fwhm(2),-lim(2):lim(2))'; krn2 = krn2/sum(krn2); H = conv2(H,krn2);
% Compute cost function from histogram
H = H+eps;
sh = sum(H(:));
H = H/sh;
s1 = sum(H,1);
s2 = sum(H,2);
switch lower(cf)
case 'mi',
% Mutual Information:
H = H.*log2(H./(s2*s1));
mi = sum(H(:));
o = -mi;
case 'ecc',
% Entropy Correlation Coefficient of:
% Maes, Collignon, Vandermeulen, Marchal & Suetens (1997).
% "Multimodality image registration by maximisation of mutual
% information". IEEE Transactions on Medical Imaging 16(2):187-198
H = H.*log2(H./(s2*s1));
mi = sum(H(:));
ecc = -2*mi/(sum(s1.*log2(s1))+sum(s2.*log2(s2)));
o = -ecc;
case 'nmi',
% Normalised Mutual Information of:
% Studholme, Hill & Hawkes (1998).
% "A normalized entropy measure of 3-D medical image alignment".
% in Proc. Medical Imaging 1998, vol. 3338, San Diego, CA, pp. 132-143.
nmi = (sum(s1.*log2(s1))+sum(s2.*log2(s2)))/sum(sum(H.*log2(H)));
o = -nmi;
case 'ncc',
% Normalised Cross Correlation
i = 1:size(H,1);
j = 1:size(H,2);
m1 = sum(s2.*i');
m2 = sum(s1.*j);
sig1 = sqrt(sum(s2.*(i'-m1).^2));
sig2 = sqrt(sum(s1.*(j -m2).^2));
[i,j] = ndgrid(i-m1,j-m2);
ncc = sum(sum(H.*i.*j))/(sig1*sig2);
o = -ncc;
otherwise,
error('Invalid cost function specified');
end;
return;
%_______________________________________________________________________
%_______________________________________________________________________
function udat = loaduint8(V)
% Load data from file indicated by V into an array of unsigned bytes.
if size(V.pinfo,2)==1 && V.pinfo(1) == 2,
mx = 255*V.pinfo(1) + V.pinfo(2);
mn = V.pinfo(2);
else
spm_progress_bar('Init',V.dim(3),...
['Computing max/min of ' spm_str_manip(V.fname,'t')],...
'Planes complete');
mx = -Inf; mn = Inf;
for p=1:V.dim(3),
img = spm_slice_vol(V,spm_matrix([0 0 p]),V.dim(1:2),1);
mx = max([max(img(:))+paccuracy(V,p) mx]);
mn = min([min(img(:)) mn]);
spm_progress_bar('Set',p);
end;
end;
% Another pass to find a maximum that allows a few hot-spots in the data.
spm_progress_bar('Init',V.dim(3),...
['2nd pass max/min of ' spm_str_manip(V.fname,'t')],...
'Planes complete');
nh = 2048;
h = zeros(nh,1);
for p=1:V.dim(3),
img = spm_slice_vol(V,spm_matrix([0 0 p]),V.dim(1:2),1);
img = img(isfinite(img));
img = round((img+((mx-mn)/(nh-1)-mn))*((nh-1)/(mx-mn)));
if spm_matlab_version_chk('7.0')>=0,
h = h + accumarray(img,1,[nh 1]);
else
h = h + full(sparse(img,1,1,nh,1));
end
spm_progress_bar('Set',p);
end;
tmp = [find(cumsum(h)/sum(h)>0.9999); nh];
mx = (mn*nh-mx+tmp(1)*(mx-mn))/(nh-1);
spm_progress_bar('Init',V.dim(3),...
['Loading ' spm_str_manip(V.fname,'t')],...
'Planes loaded');
%udat = zeros(V.dim,'uint8'); Needs MATLAB 7 onwards
udat = uint8(0);
udat(V.dim(1),V.dim(2),V.dim(3)) = 0;
rand('state',100);
for p=1:V.dim(3),
img = spm_slice_vol(V,spm_matrix([0 0 p]),V.dim(1:2),1);
acc = paccuracy(V,p);
if acc==0,
udat(:,:,p) = uint8(max(min(round((img-mn)*(255/(mx-mn))),255),0));
else
% Add random numbers before rounding to reduce aliasing artifact
r = rand(size(img))*acc;
udat(:,:,p) = uint8(max(min(round((img+r-mn)*(255/(mx-mn))),255),0));
end;
spm_progress_bar('Set',p);
end;
spm_progress_bar('Clear');
return;
function acc = paccuracy(V,p)
if ~spm_type(V.dt(1),'intt'),
acc = 0;
else
if size(V.pinfo,2)==1,
acc = abs(V.pinfo(1,1));
else
acc = abs(V.pinfo(1,p));
end;
end;
%_______________________________________________________________________
%_______________________________________________________________________
function V = smooth_uint8(V,fwhm)
% Convolve the volume in memory (fwhm in voxels).
lim = ceil(2*fwhm);
x = -lim(1):lim(1); x = smoothing_kernel(fwhm(1),x); x = x/sum(x);
y = -lim(2):lim(2); y = smoothing_kernel(fwhm(2),y); y = y/sum(y);
z = -lim(3):lim(3); z = smoothing_kernel(fwhm(3),z); z = z/sum(z);
i = (length(x) - 1)/2;
j = (length(y) - 1)/2;
k = (length(z) - 1)/2;
spm_conv_vol(V.uint8,V.uint8,x,y,z,-[i j k]);
return;
%_______________________________________________________________________
%_______________________________________________________________________
function krn = smoothing_kernel(fwhm,x)
% Variance from FWHM
s = (fwhm/sqrt(8*log(2)))^2+eps;
% The simple way to do it. Not good for small FWHM
% krn = (1/sqrt(2*pi*s))*exp(-(x.^2)/(2*s));
% For smoothing images, one should really convolve a Gaussian
% with a sinc function. For smoothing histograms, the
% kernel should be a Gaussian convolved with the histogram
% basis function used. This function returns a Gaussian
% convolved with a triangular (1st degree B-spline) basis
% function.
% Gaussian convolved with 0th degree B-spline
% int(exp(-((x+t))^2/(2*s))/sqrt(2*pi*s),t= -0.5..0.5)
% w1 = 1/sqrt(2*s);
% krn = 0.5*(erf(w1*(x+0.5))-erf(w1*(x-0.5)));
% Gaussian convolved with 1st degree B-spline
% int((1-t)*exp(-((x+t))^2/(2*s))/sqrt(2*pi*s),t= 0..1)
% +int((t+1)*exp(-((x+t))^2/(2*s))/sqrt(2*pi*s),t=-1..0)
w1 = 0.5*sqrt(2/s);
w2 = -0.5/s;
w3 = sqrt(s/2/pi);
krn = 0.5*(erf(w1*(x+1)).*(x+1) + erf(w1*(x-1)).*(x-1) - 2*erf(w1*x ).* x)...
+w3*(exp(w2*(x+1).^2) + exp(w2*(x-1).^2) - 2*exp(w2*x.^2));
krn(krn<0) = 0;
return;
%_______________________________________________________________________
%_______________________________________________________________________
function display_results(VG,VF,x,flags)
fig = spm_figure('FindWin','Graphics');
if isempty(fig), return; end;
set(0,'CurrentFigure',fig);
spm_figure('Clear','Graphics');
%txt = 'Information Theoretic Coregistration';
switch lower(flags.cost_fun)
case 'mi', txt = 'Mutual Information Coregistration';
case 'ecc', txt = 'Entropy Correlation Coefficient Registration';
case 'nmi', txt = 'Normalised Mutual Information Coregistration';
case 'ncc', txt = 'Normalised Cross Correlation';
otherwise, error('Invalid cost function specified');
end;
% Display text
%-----------------------------------------------------------------------
ax = axes('Position',[0.1 0.8 0.8 0.15],'Visible','off','Parent',fig);
text(0.5,0.7, txt,'FontSize',16,...
'FontWeight','Bold','HorizontalAlignment','center','Parent',ax);
Q = inv(VF.mat\spm_matrix(x(:)')*VG.mat);
text(0,0.5, sprintf('X1 = %0.3f*X %+0.3f*Y %+0.3f*Z %+0.3f',Q(1,:)),'Parent',ax);
text(0,0.3, sprintf('Y1 = %0.3f*X %+0.3f*Y %+0.3f*Z %+0.3f',Q(2,:)),'Parent',ax);
text(0,0.1, sprintf('Z1 = %0.3f*X %+0.3f*Y %+0.3f*Z %+0.3f',Q(3,:)),'Parent',ax);
% Display joint histograms
%-----------------------------------------------------------------------
ax = axes('Position',[0.1 0.5 0.35 0.3],'Visible','off','Parent',fig);
H = spm_hist2(VG.uint8,VF.uint8,VF.mat\VG.mat,[1 1 1]);
tmp = log(H+1);
image(tmp*(64/max(tmp(:))),'Parent',ax');
set(ax,'DataAspectRatio',[1 1 1],...
'PlotBoxAspectRatioMode','auto','XDir','normal','YDir','normal',...
'XTick',[],'YTick',[]);
title('Original Joint Histogram','Parent',ax);
xlabel(spm_str_manip(VG.fname,'k22'),'Parent',ax);
ylabel(spm_str_manip(VF.fname,'k22'),'Parent',ax);
H = spm_hist2(VG.uint8,VF.uint8,VF.mat\spm_matrix(x(:)')*VG.mat,[1 1 1]);
ax = axes('Position',[0.6 0.5 0.35 0.3],'Visible','off','Parent',fig);
tmp = log(H+1);
image(tmp*(64/max(tmp(:))),'Parent',ax');
set(ax,'DataAspectRatio',[1 1 1],...
'PlotBoxAspectRatioMode','auto','XDir','normal','YDir','normal',...
'XTick',[],'YTick',[]);
title('Final Joint Histogram','Parent',ax);
xlabel(spm_str_manip(VG.fname,'k22'),'Parent',ax);
ylabel(spm_str_manip(VF.fname,'k22'),'Parent',ax);
% Display ortho-views
%-----------------------------------------------------------------------
spm_orthviews('Reset');
spm_orthviews('Image',VG,[0.01 0.01 .48 .49]);
h2 = spm_orthviews('Image',VF,[.51 0.01 .48 .49]);
global st
st.vols{h2}.premul = inv(spm_matrix(x(:)'));
spm_orthviews('Space');
spm_print
return; |
