unsigned ThreadFunc( myargument arg )
{
float *bias,*Estimate,*ima,*means,*variances,*average;
double epsilon,mu1,var1,totalweight,wmax,t1,t1i,t2,d,w,distanciaminima;
unsigned char *Label;
int rows,cols,slices,ini,fin,v,f,init,i,j,k,rc,ii,jj,kk,ni,nj,nk,Ndims;
extern int rician;
extern double max;
rows = arg.rows;
cols = arg.cols;
slices = arg.slices;
ini = arg.ini;
fin = arg.fin;
ima = arg.in_image;
means = arg.means_image;
variances = arg.var_image;
Estimate = arg.estimate;
bias = arg.bias;
Label = arg.label;
v = arg.radioB;
f = arg.radioS;
/* filter */
epsilon = 0.00001;
mu1 = 0.95;
var1 = 0.5;
init = 0;
rc = rows*cols;
Ndims = (2*f+1)*(2*f+1)*(2*f+1);
average = (float*)malloc(Ndims*sizeof(float));
wmax = 0.0;
for (k = ini;k<fin;k+= 2)
for (j = 0;j<rows;j+= 2)
for (i = 0;i<cols;i+= 2)
{
/* init */
for (init = 0 ; init < Ndims; init++) average[init] = 0.0;
totalweight = 0.0;
distanciaminima = 1e15;
if (ima[k*rc+(j*cols)+i]>0 && (means[k*rc+(j*cols)+i])>epsilon && (variances[k*rc+(j*cols)+i]>epsilon))
{
/* calculate minimum distance */
for (kk = -v;kk<= v;kk++)
{
nk = k+kk;
for (jj = -v;jj<= v;jj++)
{
nj = j+jj;
for (ii = -v;ii<= v;ii++)
{
ni = i+ii;
if (ii == 0 && jj == 0 && kk == 0) continue;
if (ni>= 0 && nj>= 0 && nk>= 0 && ni<cols && nj<rows && nk<slices)
{
if (ima[nk*rc+(nj*cols)+ni]>0 && (means[nk*(rc)+(nj*cols)+ni])> epsilon && (variances[nk*rc+(nj*cols)+ni]>epsilon))
{
t1 = ((double)means[k*rc+(j*cols)+i])/((double)means[nk*rc+(nj*cols)+ni]);
t1i = (max-(double)means[k*(rc)+(j*cols)+i])/(max-(double)means[nk*(rc)+(nj*cols)+ni]);
t2 = ((double)variances[k*rc+(j*cols)+i])/((double)variances[nk*rc+(nj*cols)+ni]);
if ( (t1>mu1 && t1<(1/mu1)) || (t1i>mu1 && t1i<(1/mu1)) && t2>var1 && t2<(1/var1))
{
d = distance2(ima,means,i,j,k,ni,nj,nk,f,cols,rows,slices);
if (d<distanciaminima) distanciaminima = d;
}
}
}
}
}
}
if (distanciaminima == 0) distanciaminima = 1;
/* rician correction */
if (rician)
{
for (kk = -f;kk<= f;kk++)
{
nk = k+kk;
for (ii = -f;ii<= f;ii++)
{
ni = i+ii;
for (jj = -f;jj<= f;jj++)
{
nj = j+jj;
if (ni>= 0 && nj>= 0 && nk>= 0 && ni<cols && nj<rows && nk<slices)
{
if (distanciaminima == 1e15) bias[nk*(rc)+(nj*cols)+ni] = 0.0;
else bias[nk*(rc)+(nj*cols)+ni] = (float)distanciaminima;
}
}
}
}
}
/* block filtering */
for (kk = -v;kk<= v;kk++)
{
nk = k+kk;
for (jj = -v;jj<= v;jj++)
{
nj = j+jj;
for (ii = -v;ii<= v;ii++)
{
ni = i+ii;
if (ii == 0 && jj == 0 && kk == 0) continue;
if (ni>= 0 && nj>= 0 && nk>= 0 && ni<cols && nj<rows && nk<slices)
{
if (ima[nk*rc+(nj*cols)+ni]>0 && (means[nk*(rc)+(nj*cols)+ni])> epsilon && (variances[nk*rc+(nj*cols)+ni]>epsilon))
{
t1 = ((double)means[k*rc+(j*cols)+i])/((double)means[nk*rc+(nj*cols)+ni]);
t1i = (max-(double)means[k*(rc)+(j*cols)+i])/(max-(double)means[nk*(rc)+(nj*cols)+ni]);
t2 = ((double)variances[k*rc+(j*cols)+i])/((double)variances[nk*rc+(nj*cols)+ni]);
if ( (t1>mu1 && t1<(1/mu1)) || (t1i>mu1 && t1i<(1/mu1)) && t2>var1 && t2<(1/var1))
{
d = distance(ima,i,j,k,ni,nj,nk,f,cols,rows,slices);
if (d>3*distanciaminima) w = 0;
else w = exp(-d/distanciaminima);
if (w>wmax) wmax = w;
if (w>0)
{
Average_block(ima,ni,nj,nk,f,average,w,cols,rows,slices);
totalweight = totalweight + w;
}
}
}
}
}
}
}
if (wmax == 0.0) wmax = 1.0;
Average_block(ima,i,j,k,f,average,wmax,cols,rows,slices);
totalweight = totalweight + wmax;
Value_block(Estimate,Label,i,j,k,f,average,totalweight,cols,rows,slices);
}
else
{
wmax = 1.0;
Average_block(ima,i,j,k,f,average,wmax,cols,rows,slices);
totalweight = totalweight + wmax;
Value_block(Estimate,Label,i,j,k,f,average,totalweight,cols,rows,slices);
}
}
free(average);
return 0;
}
void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
{
mxArray *xData;
double *ima, *fima,*average;
mxArray *Mxmeans, *Mxvariances, *MxEstimate, *MxLabel;
double *means, *varianzas, *Estimate, *Label;
mxArray *pv;
double label,estimate,epsilon,mu1,var1,h,w,totalweight,wmax,d,mean,var,t1,t2,hh;
int init,Ndims,i,j,k,ii,jj,kk,ni,nj,nk,v,f,ndim,indice;
const int *dims;
/*Copy input pointer x*/
xData = prhs[0];
/*Get matrix x*/
ima = mxGetPr(xData);
ndim = mxGetNumberOfDimensions(prhs[0]);
dims= mxGetDimensions(prhs[0]);
/*Copy input parameters*/
pv = prhs[1];
/*Get the Integer*/
v = (int)(mxGetScalar(pv));
pv = prhs[2];
f = (int)(mxGetScalar(pv));
pv = prhs[3];
h = (double)(mxGetScalar(pv));
hh=2*h*h;
Ndims = pow((2*f+1),ndim);
/*Allocate memory and assign output pointer*/
plhs[0] = mxCreateNumericArray(ndim,dims,mxDOUBLE_CLASS, mxREAL);
Mxmeans = mxCreateNumericArray(ndim,dims,mxDOUBLE_CLASS, mxREAL);
Mxvariances = mxCreateNumericArray(ndim,dims,mxDOUBLE_CLASS, mxREAL);
MxEstimate = mxCreateNumericArray(ndim,dims,mxDOUBLE_CLASS, mxREAL);
MxLabel = mxCreateNumericArray(ndim,dims,mxDOUBLE_CLASS, mxREAL);
average=(double*) malloc(Ndims*sizeof(double));
/*Get a pointer to the data space in our newly allocated memory*/
fima = mxGetPr(plhs[0]);
means = mxGetPr(Mxmeans);
varianzas = mxGetPr(Mxvariances);
Estimate = mxGetPr(MxEstimate);
Label = mxGetPr(MxLabel);
for (i = 0; i < dims[2] *dims[1] * dims[0];i++)
{
Estimate[i] = 0.0;
Label[i] = 0.0;
fima[i] = 0.0;
}
for(k=0;k<dims[2];k++)
{
for(i=0;i<dims[1];i++)
{
for(j=0;j<dims[0];j++)
{
mean=0;
indice=0;
for(ii=-1;ii<=1;ii++)
{
for(jj=-1;jj<=1;jj++)
{
for(kk=-1;kk<=1;kk++)
{
ni=i+ii;
nj=j+jj;
nk=k+kk;
if(ni<0) ni=-ni;
if(nj<0) nj=-nj;
if(nk<0) nk=-nk;
if(ni>=dims[1]) ni=2*dims[1]-ni-1;
if(nj>=dims[0]) nj=2*dims[0]-nj-1;
if(nk>=dims[2]) nk=2*dims[2]-nk-1;
mean = mean + ima[nk*(dims[0]*dims[1])+(ni*dims[0])+nj];
indice=indice+1;
}
}
}
mean=mean/indice;
means[k*(dims[0]*dims[1])+(i*dims[0])+j]=mean;
}
}
}
for(k=0;k<dims[2];k++)
{
for(i=0;i<dims[1];i++)
{
for(j=0;j<dims[0];j++)
{
var=0;
indice=0;
for(ii=-1;ii<=1;ii++)
{
for(jj=-1;jj<=1;jj++)
{
for(kk=-1;kk<=1;kk++)
{
ni=i+ii;
nj=j+jj;
nk=k+kk;
if(ni>=0 && nj>=0 && nk>0 && ni<dims[1] && nj<dims[0] && nk<dims[2])
{
var = var + (ima[nk*(dims[0]*dims[1])+(ni*dims[0])+nj]-means[k*(dims[0]*dims[1])+(i*dims[0])+j])*(ima[nk*(dims[0]*dims[1])+(ni*dims[0])+nj]-means[k*(dims[0]*dims[1])+(i*dims[0])+j]);
indice=indice+1;
}
}
}
}
var=var/(indice-1);
varianzas[k*(dims[0]*dims[1])+(i*dims[0])+j]=var;
}
}
}
/*filter*/
epsilon = 0.00001;
mu1 = 0.95;
var1 = 0.5;
init = 0;
for(k=0;k<dims[2];k+=2)
{
for(i=0;i<dims[1];i+=2)
{
for(j=0;j<dims[0];j+=2)
{
for (init=0 ; init < Ndims; init++)
{
average[init]=0.0;
}
/*average=0;*/
totalweight=0.0;
if ((means[k*(dims[0]*dims[1])+(i*dims[0])+j])>epsilon && (varianzas[k*(dims[0]*dims[1])+(i*dims[0])+j]>epsilon))
{
wmax=0.0;
for(kk=-v;kk<=v;kk++)
{
for(ii=-v;ii<=v;ii++)
{
for(jj=-v;jj<=v;jj++)
{
ni=i+ii;
nj=j+jj;
nk=k+kk;
if(ii==0 && jj==0 && kk==0) continue;
if(ni>=0 && nj>=0 && nk>=0 && ni<dims[1] && nj<dims[0] && nk<dims[2])
{
if ((means[nk*(dims[0]*dims[1])+(ni*dims[0])+nj])> epsilon && (varianzas[nk*(dims[0]*dims[1])+(ni*dims[0])+nj]>epsilon))
{
t1 = (means[k*(dims[0]*dims[1])+(i*dims[0])+j])/(means[nk*(dims[0]*dims[1])+(ni*dims[0])+nj]);
t2 = (varianzas[k*(dims[0]*dims[1])+(i*dims[0])+j])/(varianzas[nk*(dims[0]*dims[1])+(ni*dims[0])+nj]);
if(t1>mu1 && t1<(1/mu1) && t2>var1 && t2<(1/var1))
{
d=distance(ima,i,j,k,ni,nj,nk,f,dims[1],dims[0],dims[2]);
w = exp(-d/(h*h));
if(w>wmax) wmax = w;
Average_block(ima,ni,nj,nk,f,average,w,dims);
/*average = average + w*ima[nk*(dims[0]*dims[1])+(ni*dims[0])+nj];*/
totalweight = totalweight + w;
}
}
}
}
}
}
if(wmax==0.0) wmax=1.0;
/*average = average + wmax*ima[k*(dims[0]*dims[1])+(i*dims[0])+j];*/
Average_block(ima,i,j,k,f,average,wmax,dims);
totalweight = totalweight + wmax;
/*fima[k*(dims[0]*dims[1])+(i*dims[0])+j] = average / totalweight;*/
if(totalweight != 0.0)
Value_block(Estimate,Label,i,j,k,f,average,totalweight,dims,hh);
}
else
{
wmax=1.0;
Average_block(ima,i,j,k,f,average,wmax,dims);
totalweight = totalweight + wmax;
Value_block(Estimate,Label,i,j,k,f,average,totalweight,dims,hh);
}
}
}
}
label = 0.0;
estimate = 0.0;
/* Aggregation of the estimators (i.e. means computation) */
for (k = 0; k < dims[2]; k++ )
{
for (i = 0; i < dims[1]; i++ )
{
for (j = 0; j < dims[0]; j++ )
{
label = Label[k*(dims[0]*dims[1])+(i*dims[0])+j];
if (label == 0.0)
{
fima[k*(dims[0]*dims[1])+(i*dims[1])+j] = ima[k*(dims[0]*dims[1])+(i*dims[0])+j];
}
else
{
estimate = Estimate[k*(dims[0]*dims[1])+(i*dims[0])+j];
estimate = (estimate/label);
fima[k*(dims[0]*dims[1])+(i*dims[0])+j]=estimate;
}
}
}
}
return;
}
