segmentation brain tissues with pso
18 vues (au cours des 30 derniers jours)
Afficher commentaires plus anciens
hello i'm using this function to do segmentation brain tissues like the picture but i don't why it doesn't working ,i waant extract level in 3 images. any one could help me plz
function [Iout,intensity,fitness,time]=pso(I,level,method)
tic;
if (nargin<2) %didn't choose level and method
level=2;
method='pso';
end
if (nargin<3) %didn't choose method
method='pso';
end
if size(I,3)==1 %grayscale image
[n_countR,x_valueR] = imhist(I(:,:,1));
elseif size(I,3)==3 %RGB image
[n_countR,x_valueR] = imhist(I(:,:,1));
[n_countG,x_valueG] = imhist(I(:,:,2));
[n_countB,x_valueB] = imhist(I(:,:,3));
end
Nt=size(I,1)*size(I,2);
Lmax=256; %256 different maximum levels are considered in an image (i.e., 0 to 255)
for i=1:Lmax
if size(I,3)==1 %grayscale image
probR(i)=n_countR(i)/Nt;
elseif size(I,3)==3 %RGB image
probR(i)=n_countR(i)/Nt;
probG(i)=n_countG(i)/Nt;
probB(i)=n_countB(i)/Nt;
end
end
if strcmpi(method,'pso') %PSO method
N = 150; %predefined PSO population for multi-segmentation
N_PAR = level-1; %number of thresholds (number of levels-1)
N_GER = 150; %number of iterations of the PSO algorithm
PHI1 = 0.8; %individual weight of particles
PHI2 = 0.8; %social weight of particles
W = 1.2; %inertial factor
vmin=-5;
vmax=5;
if size(I,3)==1 %grayscale image
vR=zeros(N,N_PAR); %velocities of particles
X_MAXR = Lmax*ones(1,N_PAR);
X_MINR = ones(1,N_PAR);
gBestR = zeros(1,N_PAR);
gbestvalueR = -10000;
gauxR = ones(N,1);
xBestR=zeros(N,N_PAR);
fitBestR=zeros(N,1);
fitR = zeros(N,1);
xR = zeros(N,N_PAR);
for i = 1: N
for j = 1: N_PAR
xR(i,j) = fix(rand(1,1) * ( X_MAXR(j)-X_MINR(j) ) + X_MINR(j));
end
end
for si=1:length(xR)
xR(si,:)=sort(xR(si,:));
end
elseif size(I,3)==3 %RGB image
vR=zeros(N,N_PAR); %velocities of particles
vG=zeros(N,N_PAR);
vB=zeros(N,N_PAR);
X_MAXR = Lmax*ones(1,N_PAR);
X_MINR = ones(1,N_PAR);
X_MAXG = Lmax*ones(1,N_PAR);
X_MING = ones(1,N_PAR);
X_MAXB = Lmax*ones(1,N_PAR);
X_MINB = ones(1,N_PAR);
gBestR = zeros(1,N_PAR);
gbestvalueR = -10000;
gauxR = ones(N,1);
xBestR=zeros(N,N_PAR);
fitBestR=zeros(N,1);
fitR = zeros(N,1);
gBestG = zeros(1,N_PAR);
gbestvalueG = -10000;
gauxG = ones(N,1);
xBestG=zeros(N,N_PAR);
fitBestG=zeros(N,1);
fitG = zeros(N,1);
gBestB = zeros(1,N_PAR);
gbestvalueB = -10000;
gauxB = ones(N,1);
xBestB=zeros(N,N_PAR);
fitBestB=zeros(N,1);
fitB = zeros(N,1);
xR = zeros(N,N_PAR);
for i = 1: N
for j = 1: N_PAR
xR(i,j) = fix(rand(1,1) * ( X_MAXR(j)-X_MINR(j) ) + X_MINR(j));
end
end
xG = zeros(N,N_PAR);
for i = 1: N
for j = 1: N_PAR
xG(i,j) = fix(rand(1,1) * ( X_MAXG(j)-X_MING(j) ) + X_MING(j));
end
end
xB = zeros(N,N_PAR);
for i = 1: N
for j = 1: N_PAR
xB(i,j) = fix(rand(1,1) * ( X_MAXB(j)-X_MINB(j) ) + X_MINB(j));
end
end
for si=1:length(xR)
xR(si,:)=sort(xR(si,:));
end
for si=1:length(xG)
xG(si,:)=sort(xG(si,:));
end
for si=1:length(xB)
xB(si,:)=sort(xB(si,:));
end
end
nger=1;
for j=1:N
if size(I,3)==1 %grayscale image
fitR(j)=sum(probR(1:xR(j,1)))*(sum((1:xR(j,1)).*probR(1:xR(j,1))/sum(probR(1:xR(j,1)))) - sum((1:Lmax).*probR(1:Lmax)) )^2;
for jlevel=2:level-1
fitR(j)=fitR(j)+sum(probR(xR(j,jlevel-1)+1:xR(j,jlevel)))*(sum((xR(j,jlevel-1)+1:xR(j,jlevel)).*probR(xR(j,jlevel-1)+1:xR(j,jlevel))/sum(probR(xR(j,jlevel-1)+1:xR(j,jlevel))))- sum((1:Lmax).*probR(1:Lmax)))^2;
end
fitR(j)=fitR(j)+sum(probR(xR(j,level-1)+1:Lmax))*(sum((xR(j,level-1)+1:Lmax).*probR(xR(j,level-1)+1:Lmax)/sum(probR(xR(j,level-1)+1:Lmax)))- sum((1:Lmax).*probR(1:Lmax)))^2;
fitBestR(j)=fitR(j);
elseif size(I,3)==3 %RGB image
fitR(j)=sum(probR(1:xR(j,1)))*(sum((1:xR(j,1)).*probR(1:xR(j,1))/sum(probR(1:xR(j,1)))) - sum((1:Lmax).*probR(1:Lmax)) )^2;
for jlevel=2:level-1
fitR(j)=fitR(j)+sum(probR(xR(j,jlevel-1)+1:xR(j,jlevel)))*(sum((xR(j,jlevel-1)+1:xR(j,jlevel)).*probR(xR(j,jlevel-1)+1:xR(j,jlevel))/sum(probR(xR(j,jlevel-1)+1:xR(j,jlevel))))- sum((1:Lmax).*probR(1:Lmax)))^2;
end
fitR(j)=fitR(j)+sum(probR(xR(j,level-1)+1:Lmax))*(sum((xR(j,level-1)+1:Lmax).*probR(xR(j,level-1)+1:Lmax)/sum(probR(xR(j,level-1)+1:Lmax)))- sum((1:Lmax).*probR(1:Lmax)))^2;
fitBestR(j)=fitR(j);
fitG(j)=sum(probG(1:xG(j,1)))*(sum((1:xG(j,1)).*probG(1:xG(j,1))/sum(probG(1:xG(j,1)))) - sum((1:Lmax).*probG(1:Lmax)) )^2;
for jlevel=2:level-1
fitG(j)=fitG(j)+sum(probG(xG(j,jlevel-1)+1:xG(j,jlevel)))*(sum((xG(j,jlevel-1)+1:xG(j,jlevel)).*probG(xG(j,jlevel-1)+1:xG(j,jlevel))/sum(probG(xG(j,jlevel-1)+1:xG(j,jlevel))))- sum((1:Lmax).*probG(1:Lmax)))^2;
end
fitG(j)=fitG(j)+sum(probG(xG(j,level-1)+1:Lmax))*(sum((xG(j,level-1)+1:Lmax).*probG(xG(j,level-1)+1:Lmax)/sum(probG(xG(j,level-1)+1:Lmax)))- sum((1:Lmax).*probG(1:Lmax)))^2;
fitBestG(j)=fitG(j);
fitB(j)=sum(probB(1:xB(j,1)))*(sum((1:xB(j,1)).*probB(1:xB(j,1))/sum(probB(1:xB(j,1)))) - sum((1:Lmax).*probB(1:Lmax)) )^2;
for jlevel=2:level-1
fitB(j)=fitB(j)+sum(probB(xB(j,jlevel-1)+1:xB(j,jlevel)))*(sum((xB(j,jlevel-1)+1:xB(j,jlevel)).*probB(xB(j,jlevel-1)+1:xB(j,jlevel))/sum(probB(xB(j,jlevel-1)+1:xB(j,jlevel))))- sum((1:Lmax).*probB(1:Lmax)))^2;
end
fitB(j)=fitB(j)+sum(probB(xB(j,level-1)+1:Lmax))*(sum((xB(j,level-1)+1:Lmax).*probB(xB(j,level-1)+1:Lmax)/sum(probB(xB(j,level-1)+1:Lmax)))- sum((1:Lmax).*probB(1:Lmax)))^2;
fitBestB(j)=fitB(j);
end
end
if size(I,3)==1 %grayscale image
[aR,bR]=max(fitR);
gBestR=xR(bR,:);
gbestvalueR = fitR(bR);
xBestR = xR;
elseif size(I,3)==3 %RGB image
[aR,bR]=max(fitR);
gBestR=xR(bR,:);
gbestvalueR = fitR(bR);
[aG,bG]=max(fitG);
gBestG=xG(bG,:);
gbestvalueG = fitG(bG);
[aB,bB]=max(fitB);
gBestB=xR(bB,:);
gbestvalueB = fitB(bB);
xBestR = xR;
xBestG = xG;
xBestB = xB;
end
while(nger<=N_GER)
i=1;
randnum1 = rand ([N, N_PAR]);
randnum2 = rand ([N, N_PAR]);
if size(I,3)==1 %grayscale image
vR = fix(W.*vR + randnum1.*(PHI1.*(xBestR-xR)) + randnum2.*(PHI2.*(gauxR*gBestR-xR)));
vR = ( (vR <= vmin).*vmin ) + ( (vR > vmin).*vR );
vR = ( (vR >= vmax).*vmax ) + ( (vR < vmax).*vR );
xR = xR+vR;
elseif size(I,3)==3 %RGB image
vR = fix(W.*vR + randnum1.*(PHI1.*(xBestR-xR)) + randnum2.*(PHI2.*(gauxR*gBestR-xR)));
vR = ( (vR <= vmin).*vmin ) + ( (vR > vmin).*vR );
vR = ( (vR >= vmax).*vmax ) + ( (vR < vmax).*vR );
xR = xR+vR;
vG = fix(W.*vG + randnum1.*(PHI1.*(xBestG-xG)) + randnum2.*(PHI2.*(gauxG*gBestG-xG)));
vG = ( (vG <= vmin).*vmin ) + ( (vG > vmin).*vG );
vG = ( (vG >= vmax).*vmax ) + ( (vG < vmax).*vG );
xG = xG+vG;
vB = fix(W.*vB + randnum1.*(PHI1.*(xBestB-xB)) + randnum2.*(PHI2.*(gauxB*gBestB-xB)));
vB = ( (vB <= vmin).*vmin ) + ( (vB > vmin).*vB );
vB = ( (vB >= vmax).*vmax ) + ( (vB < vmax).*vB );
xB = xB+vB;
end
if size(I,3)==1 %grayscale image
xR = ( (xR <= X_MINR(1)).*X_MINR(1) ) + ( (xR > X_MINR(1)).*xR );
xR = ( (xR >= X_MAXR(1)).*X_MAXR(1) ) + ( (xR < X_MAXR(1)).*xR );
elseif size(I,3)==3 %RGB image
xR = ( (xR <= X_MINR(1)).*X_MINR(1) ) + ( (xR > X_MINR(1)).*xR );
xR = ( (xR >= X_MAXR(1)).*X_MAXR(1) ) + ( (xR < X_MAXR(1)).*xR );
xG = ( (xG <= X_MING(1)).*X_MING(1) ) + ( (xG > X_MING(1)).*xG );
xG = ( (xG >= X_MAXG(1)).*X_MAXG(1) ) + ( (xG < X_MAXG(1)).*xG );
xB = ( (xB <= X_MINB(1)).*X_MINB(1) ) + ( (xB > X_MINB(1)).*xB );
xB = ( (xB >= X_MAXB(1)).*X_MAXB(1) ) + ( (xB < X_MAXB(1)).*xB );
end
for j = 1:N
for k = 1:N_PAR
if size(I,3)==1 %grayscale image
if (k==1)&&(k~=N_PAR)
if xR(j,k) < X_MINR(k)
xR(j,k) = X_MINR(k);
elseif xR(j,k) > xR(j,k+1)
xR(j,k) = xR(j,k+1);
end
end
if ((k>1)&&(k<N_PAR))
if xR(j,k) < xR(j,k-1)
xR(j,k) = xR(j,k-1);
elseif xR(j,k) > xR(j,k+1)
xR(j,k) = xR(j,k+1);
end
end
if (k==N_PAR)&&(k~=1)
if xR(j,k) < xR(j,k-1)
xR(j,k) = xR(j,k-1);
elseif xR(j,k) > X_MAXR(k)
xR(j,k) = X_MAXR(k);
end
end
if (k==1)&&(k==N_PAR)
if xR(j,k) < X_MINR(k)
xR(j,k) = X_MINR(k);
elseif xR(j,k) > X_MAXR(k)
xR(j,k) = X_MAXR(k);
end
end
elseif size(I,3)==3 %RGB image
if (k==1)&&(k~=N_PAR)
if xR(j,k) < X_MINR(k)
xR(j,k) = X_MINR(k);
elseif xR(j,k) > xR(j,k+1)
xR(j,k) = xR(j,k+1);
end
end
if ((k>1)&&(k<N_PAR))
if xR(j,k) < xR(j,k-1)
xR(j,k) = xR(j,k-1);
elseif xR(j,k) > xR(j,k+1)
xR(j,k) = xR(j,k+1);
end
end
if (k==N_PAR)&&(k~=1)
if xR(j,k) < xR(j,k-1)
xR(j,k) = xR(j,k-1);
elseif xR(j,k) > X_MAXR(k)
xR(j,k) = X_MAXR(k);
end
end
if (k==1)&&(k==N_PAR)
if xR(j,k) < X_MINR(k)
xR(j,k) = X_MINR(k);
elseif xR(j,k) > X_MAXR(k)
xR(j,k) = X_MAXR(k);
end
end
if (k==1)&&(k~=N_PAR)
if xG(j,k) < X_MING(k)
xG(j,k) = X_MING(k);
elseif xG(j,k) > xG(j,k+1)
xG(j,k) = xG(j,k+1);
% disp ('passou o max');
end
end
if ((k>1)&&(k<N_PAR))
if xG(j,k) < xG(j,k-1)
xG(j,k) = xG(j,k-1);
% disp ('passou o min');
elseif xG(j,k) > xG(j,k+1)
xG(j,k) = xG(j,k+1);
% disp ('passou o max');
end
end
if (k==N_PAR)&&(k~=1)
if xG(j,k) < xG(j,k-1)
xG(j,k) = xG(j,k-1);
% disp ('passou o min');
elseif xG(j,k) > X_MAXG(k)
xG(j,k) = X_MAXG(k);
% disp ('passou o max');
end
end
if (k==1)&&(k==N_PAR)
if xG(j,k) < X_MING(k)
xG(j,k) = X_MING(k);
elseif xG(j,k) > X_MAXG(k)
xG(j,k) = X_MAXG(k);
end
end
if (k==1)&&(k~=N_PAR)
if xB(j,k) < X_MINB(k)
xB(j,k) = X_MINB(k);
% disp ('passou o min');
elseif xB(j,k) > xB(j,k+1)
xB(j,k) = xB(j,k+1);
% disp ('passou o max');
end
end
if ((k>1)&&(k<N_PAR))
if xB(j,k) < xB(j,k-1)
xB(j,k) = xB(j,k-1);
% disp ('passou o min');
elseif xB(j,k) > xB(j,k+1)
xB(j,k) = xB(j,k+1);
% disp ('passou o max');
end
end
if (k==N_PAR)&&(k~=1)
if xB(j,k) < xB(j,k-1)
xB(j,k) = xB(j,k-1);
% disp ('passou o min');
elseif xB(j,k) > X_MAXB(k)
xB(j,k) = X_MAXB(k);
% disp ('passou o max');
end
end
if (k==1)&&(k==N_PAR)
if xB(j,k) < X_MINB(k)
xB(j,k) = X_MINB(k);
elseif xB(j,k) > X_MAXB(k)
xB(j,k) = X_MAXB(k);
end
end
end
end
end
while(i<=N)
if(i==N)
for j=1:N
if size(I,3)==1 %grayscale image
fitR(j)=sum(probR(1:xR(j,1)))*(sum((1:xR(j,1)).*probR(1:xR(j,1))/sum(probR(1:xR(j,1)))) - sum((1:Lmax).*probR(1:Lmax)) )^2;
for jlevel=2:level-1
fitR(j)=fitR(j)+sum(probR(xR(j,jlevel-1)+1:xR(j,jlevel)))*(sum((xR(j,jlevel-1)+1:xR(j,jlevel)).*probR(xR(j,jlevel-1)+1:xR(j,jlevel))/sum(probR(xR(j,jlevel-1)+1:xR(j,jlevel))))- sum((1:Lmax).*probR(1:Lmax)))^2;
end
fitR(j)=fitR(j)+sum(probR(xR(j,level-1)+1:Lmax))*(sum((xR(j,level-1)+1:Lmax).*probR(xR(j,level-1)+1:Lmax)/sum(probR(xR(j,level-1)+1:Lmax)))- sum((1:Lmax).*probR(1:Lmax)))^2;
if fitR(j) > fitBestR(j)
fitBestR(j) = fitR(j);
xBestR(j,:) = xR(j,:);
end
elseif size(I,3)==3 %RGB image
fitR(j)=sum(probR(1:xR(j,1)))*(sum((1:xR(j,1)).*probR(1:xR(j,1))/sum(probR(1:xR(j,1)))) - sum((1:Lmax).*probR(1:Lmax)) )^2;
for jlevel=2:level-1
fitR(j)=fitR(j)+sum(probR(xR(j,jlevel-1)+1:xR(j,jlevel)))*(sum((xR(j,jlevel-1)+1:xR(j,jlevel)).*probR(xR(j,jlevel-1)+1:xR(j,jlevel))/sum(probR(xR(j,jlevel-1)+1:xR(j,jlevel))))- sum((1:Lmax).*probR(1:Lmax)))^2;
end
fitR(j)=fitR(j)+sum(probR(xR(j,level-1)+1:Lmax))*(sum((xR(j,level-1)+1:Lmax).*probR(xR(j,level-1)+1:Lmax)/sum(probR(xR(j,level-1)+1:Lmax)))- sum((1:Lmax).*probR(1:Lmax)))^2;
fitG(j)=sum(probG(1:xG(j,1)))*(sum((1:xG(j,1)).*probG(1:xG(j,1))/sum(probG(1:xG(j,1)))) - sum((1:Lmax).*probG(1:Lmax)) )^2;
for jlevel=2:level-1
fitG(j)=fitG(j)+sum(probG(xG(j,jlevel-1)+1:xG(j,jlevel)))*(sum((xG(j,jlevel-1)+1:xG(j,jlevel)).*probG(xG(j,jlevel-1)+1:xG(j,jlevel))/sum(probG(xG(j,jlevel-1)+1:xG(j,jlevel))))- sum((1:Lmax).*probG(1:Lmax)))^2;
end
fitG(j)=fitG(j)+sum(probG(xG(j,level-1)+1:Lmax))*(sum((xG(j,level-1)+1:Lmax).*probG(xG(j,level-1)+1:Lmax)/sum(probG(xG(j,level-1)+1:Lmax)))- sum((1:Lmax).*probG(1:Lmax)))^2;
fitB(j)=sum(probB(1:xB(j,1)))*(sum((1:xB(j,1)).*probB(1:xB(j,1))/sum(probB(1:xB(j,1)))) - sum((1:Lmax).*probB(1:Lmax)) )^2;
for jlevel=2:level-1
fitB(j)=fitB(j)+sum(probB(xB(j,jlevel-1)+1:xB(j,jlevel)))*(sum((xB(j,jlevel-1)+1:xB(j,jlevel)).*probB(xB(j,jlevel-1)+1:xB(j,jlevel))/sum(probB(xB(j,jlevel-1)+1:xB(j,jlevel))))- sum((1:Lmax).*probB(1:Lmax)))^2;
end
fitB(j)=fitB(j)+sum(probB(xB(j,level-1)+1:Lmax))*(sum((xB(j,level-1)+1:Lmax).*probB(xB(j,level-1)+1:Lmax)/sum(probB(xB(j,level-1)+1:Lmax)))- sum((1:Lmax).*probB(1:Lmax)))^2;
if fitR(j) > fitBestR(j)
fitBestR(j) = fitR(j);
xBestR(j,:) = xR(j,:);
end
if fitG(j) > fitBestG(j)
fitBestG(j) = fitG(j);
xBestG(j,:) = xG(j,:);
end
if fitB(j) > fitBestB(j)
fitBestB(j) = fitB(j);
xBestB(j,:) = xB(j,:);
end
end
end
if size(I,3)==1 %grayscale image
[aR,bR] = max (fitR);
if (fitR(bR) > gbestvalueR)
gBestR=xR(bR,:)-1;
gbestvalueR = fitR(bR);
end
elseif size(I,3)==3 %RGB image
[aR,bR] = max (fitR);
[aG,bG] = max (fitG);
[aB,bB] = max (fitB);
if (fitR(bR) > gbestvalueR)
gBestR=xR(bR,:)-1;
gbestvalueR = fitR(bR);
end
if (fitG(bG) > gbestvalueG)
gBestG=xG(bG,:)-1;
gbestvalueG = fitG(bG);
end
if (fitB(bB) > gbestvalueB)
gBestB=xB(bB,:)-1;
gbestvalueB = fitB(bB);
end
end
nger=nger+1;
end
i=i+1;
end
end
% gbestvalueR
end
function imgOut=imageRGB(img,Rvec,Gvec,Bvec)
imgOutR=img(:,:,1);
imgOutG=img(:,:,2);
imgOutB=img(:,:,3);
Rvec=[0 Rvec 256];
for iii=1:size(Rvec,2)-1
at=find(imgOutR(:,:)>=Rvec(iii) & imgOutR(:,:)<Rvec(iii+1));
imgOutR(at)=Rvec(iii);
end
Gvec=[0 Gvec 256];
for iii=1:size(Gvec,2)-1
at=find(imgOutG(:,:)>=Gvec(iii) & imgOutG(:,:)<Gvec(iii+1));
imgOutG(at)=Gvec(iii);
end
Bvec=[0 Bvec 256];
for iii=1:size(Bvec,2)-1
at=find(imgOutB(:,:)>=Bvec(iii) & imgOutB(:,:)<Bvec(iii+1));
imgOutB(at)=Bvec(iii);
end
imgOut=img;
imgOut(:,:,1)=imgOutR;
imgOut(:,:,2)=imgOutG;
imgOut(:,:,3)=imgOutB;
function imgOut=imageGRAY(img,Rvec)
% imgOut=img;
limites=[0 Rvec 255];
tamanho=size(img);
imgOut(:,:)=img*0;
cores=colormap(lines)*255;
close all;
%tic
k=1;
for i= 1:tamanho(1,1)
for j=1:tamanho(1,2)
while(k<size(limites,2))
if(img(i,j)>=limites(1,k) && img(i,j)<=limites(1,k+1))
imgOut(i,j,1)=limites(1,k);
% imgOut(i,j,2)=cores(k,2);
% imgOut(i,j,3)=cores(k,3);
end
k=k+1;
end
k=1;
end
end
function [C,RA,RB] = insertrows(A,B,ind)
error(nargchk(2,3,nargin)) ;
if nargin==2,
% just horizontal concatenation, suggested by Tim Davis
ind = size(A,1) ;
end
% shortcut when any of the inputs are empty
if isempty(B) || isempty(ind),
C = A ;
if nargout > 1,
RA = 1:size(A,1) ;
RB = [] ;
end
return
end
sa = size(A) ;
% match the sizes of A, B
if numel(B)==1,
% B has a single argument, expand to match A
sb = [1 sa(2:end)] ;
B = repmat(B,sb) ;
else
% otherwise check for dimension errors
if ndims(A) ~= ndims(B),
error('insertrows:DimensionMismatch', ...
'Both input matrices should have the same number of dimensions.') ;
end
sb = size(B) ;
if ~all(sa(2:end) == sb(2:end)),
error('insertrows:DimensionMismatch', ...
'Both input matrices should have the same number of columns (and planes, etc).') ;
end
end
ind = ind(:) ; % make as row vector
ni = length(ind) ;
% match the sizes of B and IND
if ni ~= sb(1),
if ni==1 && sb(1) > 1,
% expand IND
ind = repmat(ind,sb(1),1) ;
elseif (ni > 1) && (sb(1)==1),
% expand B
B = repmat(B,ni,1) ;
else
error('insertrows:InputMismatch',...
'The number of rows to insert should equal the number of insertion positions.') ;
end
end
sb = size(B) ;
% the actual work
% 1. concatenate matrices
C = [A ; B] ;
% 2. sort the respective indices, the first output of sort is ignored (by
% giving it the same name as the second output, one avoids an extra
% large variable in memory)
[abi,abi] = sort([[1:sa(1)].' ; ind(:)]) ;
% 3. reshuffle the large matrix
C = C(abi,:) ;
% 4. reshape as A for nd matrices (nd>2)
if ndims(A) > 2,
sc = sa ;
sc(1) = sc(1)+sb(1) ;
C = reshape(C,sc) ;
end
if nargout > 1,
% additional outputs required
R = [zeros(sa(1),1) ; ones(sb(1),1)] ;
R = R(abi) ;
RA = find(R==0) ;
RB = find(R==1) ;
end
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