int
Matrix::multiplyWithMatrix(Matrix* b)
{
if(col!=b->getRowCnt())
{
return -1;
}
gsl_matrix* res=gsl_matrix_alloc(row,b->getColCnt());
gsl_matrix_set_zero(res);
gsl_matrix_float* resmatrix=gsl_matrix_float_alloc(row,b->getColCnt());
convertToFloat(resmatrix,res,row,b->getColCnt());
gsl_matrix_float* cmatrix=gsl_matrix_float_alloc(row,col);
convertToFloat(cmatrix,matrix,row,col);
gsl_matrix_float* bmatrix=gsl_matrix_float_alloc(b->getRowCnt(),b->getColCnt());
convertToFloat(bmatrix,b->matrix,b->getRowCnt(),b->getColCnt());
gsl_blas_sgemm(CblasNoTrans, CblasNoTrans, 1, cmatrix, bmatrix, 0, resmatrix);
convertFromFloat(resmatrix,res,row,b->getColCnt());
gsl_matrix_float_free(resmatrix);
gsl_matrix_float_free(cmatrix);
gsl_matrix_float_free(bmatrix);
gsl_matrix_memcpy(matrix, res);
gsl_matrix_free(res);
return 0;
}
//Multiply this with b
Matrix*
Matrix::multiplyMatrix(Matrix* b)
{
if(col!=b->getRowCnt())
{
return NULL;
}
Matrix* res=new Matrix(row,b->getColCnt());
gsl_matrix_set_zero (res->matrix);
gsl_matrix_float* resmatrix=gsl_matrix_float_alloc(row,b->getColCnt());
convertToFloat(resmatrix,res->matrix,row,b->getColCnt());
gsl_matrix_float* cmatrix=gsl_matrix_float_alloc(row,col);
convertToFloat(cmatrix,matrix,row,col);
gsl_matrix_float* bmatrix=gsl_matrix_float_alloc(b->getRowCnt(),b->getColCnt());
convertToFloat(bmatrix,b->matrix,b->getRowCnt(),b->getColCnt());
gsl_blas_sgemm (CblasNoTrans, CblasNoTrans, 1, cmatrix, bmatrix, 0, resmatrix);
convertFromFloat(resmatrix,res->matrix,row,b->getColCnt());
gsl_matrix_float_free(resmatrix);
gsl_matrix_float_free(cmatrix);
gsl_matrix_float_free(bmatrix);
return res;
}
float *
VCorrMatrix(VAttrList list,VImage map,VShort first,VShort length)
{
VAttrListPosn posn;
VImage src[NSLICES];
size_t b,r,c,i,j,i1,n,m,nt,last,ntimesteps,nrows,ncols,nslices;
gsl_matrix_float *mat=NULL;
float *A=NULL;
/*
** get image dimensions
*/
i = ntimesteps = nrows = ncols = 0;
for (VFirstAttr (list, & posn); VAttrExists (& posn); VNextAttr (& posn)) {
if (VGetAttrRepn (& posn) != VImageRepn) continue;
VGetAttrValue (& posn, NULL,VImageRepn, & src[i]);
if (VPixelRepn(src[i]) != VShortRepn) continue;
if (VImageNBands(src[i]) > ntimesteps) ntimesteps = VImageNBands(src[i]);
if (VImageNRows(src[i]) > nrows) nrows = VImageNRows(src[i]);
if (VImageNColumns(src[i]) > ncols) ncols = VImageNColumns(src[i]);
i++;
if (i >= NSLICES) VError(" too many slices");
}
nslices = i;
/* get time steps to include */
if (length < 1) length = ntimesteps-2;
last = first + length -1;
if (last >= ntimesteps) last = ntimesteps-1;
if (first < 0) first = 1;
nt = last - first + 1;
i1 = first+1;
if (nt < 2) VError(" not enough timesteps, nt= %d",nt);
/* number of voxels */
n = VImageNColumns(map);
fprintf(stderr," ntimesteps: %d, first= %d, last= %d, nt= %d, nvoxels: %ld\n",
(int)ntimesteps,(int)first,(int)last,(int)nt,(long)n);
/*
** avoid casting to float, copy data to matrix
*/
mat = gsl_matrix_float_calloc(n,nt);
if (!mat) VError(" err allocating mat");
for (i=0; i<n; i++) {
b = VPixel(map,0,0,i,VShort);
r = VPixel(map,0,1,i,VShort);
c = VPixel(map,0,2,i,VShort);
float *ptr = gsl_matrix_float_ptr(mat,i,0);
int k;
j = 0;
for (k=first; k<=last; k++) {
if (j >= mat->size2) VError(" j= %d %d",j,mat->size2);
if (k >= VImageNBands(src[b])) VError(" k= %d %d",k, VImageNBands(src[b]));
*ptr++ = (float) VPixel(src[b],k,r,c,VShort);
j++;
}
}
/*
** compute similarity matrix
*/
m = (n*(n+1))/2;
fprintf(stderr," compute correlation matrix...\n");
A = (float *) VCalloc(m,sizeof(float));
if (!A) VError(" err allocating correlation matrix");
memset(A,0,m*sizeof(float));
size_t progress=0;
#pragma omp parallel for shared(progress) private(j) schedule(guided) firstprivate(mat,A)
for (i=0; i<n; i++) {
if (i%100 == 0) fprintf(stderr," %d00\r",(int)(++progress));
const float *arr1 = gsl_matrix_float_const_ptr(mat,i,0);
for (j=0; j<i; j++) {
const float *arr2 = gsl_matrix_float_const_ptr(mat,j,0);
const double v = Correlation(arr1,arr2,nt);
const size_t k=j+i*(i+1)/2;
if (k >= m) VError(" illegal addr k= %d, m= %d",k,m);
A[k] = v;
}
}
fprintf(stderr," matrix done.\n");
gsl_matrix_float_free(mat);
return A;
}
VAttrList
VNCM(VAttrList list,VImage mask,VShort minval,VShort first,VShort length,VFloat threshold)
{
VAttrList out_list=NULL;
VAttrListPosn posn;
VImage src[NSLICES],map=NULL;
VImage dest=NULL;
size_t b,r,c,i,j,i1,n,m,nt,last,ntimesteps,nrows,ncols,nslices;
gsl_matrix_float *mat=NULL;
float *A=NULL,*ev=NULL;
/*
** get image dimensions
*/
i = ntimesteps = nrows = ncols = 0;
for (VFirstAttr (list, & posn); VAttrExists (& posn); VNextAttr (& posn)) {
if (VGetAttrRepn (& posn) != VImageRepn) continue;
VGetAttrValue (& posn, NULL,VImageRepn, & src[i]);
if (VPixelRepn(src[i]) != VShortRepn) continue;
if (VImageNBands(src[i]) > ntimesteps) ntimesteps = VImageNBands(src[i]);
if (VImageNRows(src[i]) > nrows) nrows = VImageNRows(src[i]);
if (VImageNColumns(src[i]) > ncols) ncols = VImageNColumns(src[i]);
i++;
if (i >= NSLICES) VError(" too many slices");
}
nslices = i;
/* get time steps to include */
if (length < 1) length = ntimesteps-2;
last = first + length -1;
if (last >= ntimesteps) last = ntimesteps-1;
if (first < 0) first = 1;
nt = last - first + 1;
i1 = first+1;
if (nt < 2) VError(" not enough timesteps, nt= %d",nt);
fprintf(stderr,"# ntimesteps: %d, first= %d, last= %d, nt= %d\n",
(int)ntimesteps,(int)first,(int)last,(int)nt);
/* count number of voxels */
n = 0;
for (b=0; b<nslices; b++) {
if (VImageNRows(src[b]) < 2) continue;
for (r=0; r<nrows; r++) {
for (c=0; c<ncols; c++) {
if (VPixel(src[b],i1,r,c,VShort) < minval) continue;
if (VGetPixel(mask,b,r,c) < 0.5) continue;
n++;
}
}
}
fprintf(stderr," nvoxels: %ld\n",(long)n);
/*
** voxel addresses
*/
map = VCreateImage(1,5,n,VFloatRepn);
if (map == NULL) VError(" error allocating addr map");
VFillImage(map,VAllBands,0);
VPixel(map,0,3,0,VFloat) = nslices;
VPixel(map,0,3,1,VFloat) = nrows;
VPixel(map,0,3,2,VFloat) = ncols;
i = 0;
for (b=0; b<nslices; b++) {
if (VImageNRows(src[b]) < 2) continue;
for (r=0; r<nrows; r++) {
for (c=0; c<ncols; c++) {
if (VPixel(src[b],i1,r,c,VShort) < minval) continue;
if (VGetPixel(mask,b,r,c) < 0.5) continue;
VPixel(map,0,0,i,VFloat) = b;
VPixel(map,0,1,i,VFloat) = r;
VPixel(map,0,2,i,VFloat) = c;
i++;
}
}
}
/*
** avoid casting to float, copy data to matrix
*/
mat = gsl_matrix_float_calloc(n,nt);
for (i=0; i<n; i++) {
b = VPixel(map,0,0,i,VFloat);
r = VPixel(map,0,1,i,VFloat);
c = VPixel(map,0,2,i,VFloat);
float *ptr = gsl_matrix_float_ptr(mat,i,0);
int k;
j = 0;
for (k=first; k<=last; k++) {
if (j >= mat->size2) VError(" j= %d %d",j,mat->size2);
if (k >= VImageNBands(src[b])) VError(" k= %d %d",k, VImageNBands(src[b]));
*ptr++ = (float) VPixel(src[b],k,r,c,VShort);
j++;
}
}
/*
** compute similarity matrix
*/
m = (n*(n+1))/2;
fprintf(stderr," matrix computation, n= %ld...\n",(long)n);
A = (float *) calloc(m,sizeof(float));
if (!A) VError(" err allocating correlation matrix");
memset(A,0,m*sizeof(float));
size_t progress=0;
#pragma omp parallel for shared(progress) private(j) schedule(guided) firstprivate(mat,A)
for (i=0; i<n; i++) {
if (i%100 == 0) fprintf(stderr," %d00\r",(int)(++progress));
const float *arr1 = gsl_matrix_float_const_ptr(mat,i,0);
for (j=0; j<i; j++) {
const float *arr2 = gsl_matrix_float_const_ptr(mat,j,0);
const double v = Correlation(arr1,arr2,nt);
const size_t k=j+i*(i+1)/2;
if (k >= m) VError(" illegal addr k= %d, m= %d",k,m);
A[k] = (float)v;
}
}
fprintf(stderr," matrix done.\n");
gsl_matrix_float_free(mat);
/*
** eigenvector centrality
*/
ev = (float *) VCalloc(n,sizeof(float));
DegreeCentrality(A,ev,n,threshold);
dest = WriteOutput(src[0],map,nslices,nrows,ncols,ev,n);
VSetAttr(VImageAttrList(dest),"name",NULL,VStringRepn,"DegreeCM");
out_list = VCreateAttrList();
VAppendAttr(out_list,"image",NULL,VImageRepn,dest);
return out_list;
}
/*
** general linear regression
*/
VAttrList
VRegression(ListInfo *linfo, int nlists, VShort minval, VImage design, VFloat sigma, VLong itr) {
VAttrList out_list;
VImageInfo *xinfo;
int nbands = 0, nslices = 0, nrows = 0, ncols = 0, slice, row, col, nr, nc;
VImage src[NSLICES], res_image = NULL;
VImage beta_image[MBETA], BCOV = NULL, KX_image = NULL;
VImage res_map[ETMP];
float smooth_fwhm = 0, vx = 0, vy = 0, vz = 0;
VFloat *float_pp, df;
VRepnKind repn;
float d, err;
int i, k, l, n, m = 0, nt, fd = 0, npix = 0;
int i0 = 0, i1 = 0;
double u, sig, trace = 0, trace2 = 0, var = 0, sum = 0, nx = 0, mean = 0, sum2;
float *ptr1, *ptr2;
double x;
gsl_matrix_float *X = NULL, *XInv = NULL, *SX = NULL;
gsl_vector_float *y, *z, *beta, *ys;
gsl_vector *kernel;
gsl_matrix_float *S = NULL, *Vc = NULL, *F = NULL, *P = NULL, *Q = NULL;
gsl_matrix_float *R = NULL, *RV = NULL;
VBoolean smooth = TRUE; /* no smoothness estimation */
gsl_set_error_handler_off();
/*
** read input data
*/
nslices = nbands = nrows = ncols = 0;
for(k = 0; k < nlists; k++) {
n = linfo[k].nslices;
nr = linfo[k].nrows;
nc = linfo[k].ncols;
nt = linfo[k].ntimesteps;
nbands += nt;
if(nslices == 0)
nslices = n;
else if(nslices != n)
VError(" inconsistent image dimensions, slices: %d %d", n, nslices);
if(nrows == 0)
nrows = nr;
else if(nrows != nr)
VError(" inconsistent image dimensions, rows: %d %d", nr, nrows);
if(ncols == 0)
ncols = nc;
else if(ncols != nc)
VError(" inconsistent image dimensions, cols: %d %d", nc, ncols);
}
fprintf(stderr, " num images: %d, image dimensions: %d x %d x %d\n",
nlists, nslices, nrows, ncols);
/*
** get design dimensions
*/
m = VImageNRows(design); /* number of timesteps */
n = VImageNColumns(design); /* number of covariates */
fprintf(stderr, " ntimesteps=%d, num covariates=%d\n", m, n);
if(n >= MBETA)
VError(" too many covariates (%d), max is %d", n, MBETA);
if(m != nbands)
VError(" design dimension inconsistency: %d %d", m, nbands);
fprintf(stderr, " working...\n");
/*
** read design matrix
*/
X = gsl_matrix_float_alloc(m, n);
for(k = 0; k < m; k++) {
for(l = 0; l < n; l++) {
x = VGetPixel(design, 0, k, l);
fmset(X, k, l, (float)x);
}
}
/*
** pre-coloring, set up K-matrix, S=K, V = K*K^T with K=S
*/
S = gsl_matrix_float_alloc(m, m);
GaussMatrix((double)sigma, S);
Vc = fmat_x_matT(S, S, NULL);
/*
** compute pseudoinverse
*/
SX = fmat_x_mat(S, X, NULL);
XInv = fmat_PseudoInv(SX, NULL);
/*
** get variance estimate
*/
Q = fmat_x_mat(XInv, Vc, Q);
F = fmat_x_matT(Q, XInv, F);
BCOV = VCreateImage(1, n, n, VFloatRepn);
float_pp = VImageData(BCOV);
ptr1 = F->data;
for(i = 0; i < n * n; i++)
*float_pp++ = *ptr1++;
gsl_matrix_float_free(Q);
gsl_matrix_float_free(F);
/*
** get effective degrees of freedom
*/
R = gsl_matrix_float_alloc(m, m);
P = fmat_x_mat(SX, XInv, P);
gsl_matrix_float_set_identity(R);
gsl_matrix_float_sub(R, P);
RV = fmat_x_mat(R, Vc, NULL);
trace = 0;
for(i = 0; i < m; i++)
trace += fmget(RV, i, i);
P = fmat_x_mat(RV, RV, P);
trace2 = 0;
for(i = 0; i < m; i++)
trace2 += fmget(P, i, i);
df = (trace * trace) / trace2;
fprintf(stderr, " df= %.3f\n", df);
/*
** create output images
*/
xinfo = linfo[0].info;
out_list = VCreateAttrList();
res_image = VCreateImage(nslices, nrows, ncols, VFloatRepn);
VFillImage(res_image, VAllBands, 0);
VSetAttr(VImageAttrList(res_image), "name", NULL, VStringRepn, "RES/trRV");
VSetAttr(VImageAttrList(res_image), "modality", NULL, VStringRepn, "RES/trRV");
VSetAttr(VImageAttrList(res_image), "df", NULL, VFloatRepn, df);
VSetAttr(VImageAttrList(res_image), "patient", NULL, VStringRepn, xinfo->patient);
VSetAttr(VImageAttrList(res_image), "voxel", NULL, VStringRepn, xinfo->voxel);
VSetAttr(VImageAttrList(res_image), "repetition_time", NULL, VLongRepn, itr);
VSetAttr(VImageAttrList(res_image), "talairach", NULL, VStringRepn, xinfo->talairach);
/* neu */
VSetAttr(VImageAttrList(res_image),"indexOrigin",NULL,VStringRepn,xinfo->indexOrigin);
VSetAttr(VImageAttrList(res_image),"columnVec",NULL,VStringRepn,xinfo->columnVec);
VSetAttr(VImageAttrList(res_image),"rowVec",NULL,VStringRepn,xinfo->rowVec);
VSetAttr(VImageAttrList(res_image),"sliceVec",NULL,VStringRepn,xinfo->sliceVec);
VSetAttr(VImageAttrList(res_image),"FOV",NULL,VStringRepn,xinfo->FOV);
/*--------*/
if(xinfo->fixpoint[0] != 'N')
VSetAttr(VImageAttrList(res_image), "fixpoint", NULL, VStringRepn, xinfo->fixpoint);
if(xinfo->ca[0] != 'N') {
VSetAttr(VImageAttrList(res_image), "ca", NULL, VStringRepn, xinfo->ca);
VSetAttr(VImageAttrList(res_image), "cp", NULL, VStringRepn, xinfo->cp);
VSetAttr(VImageAttrList(res_image), "extent", NULL, VStringRepn, xinfo->extent);
}
VAppendAttr(out_list, "image", NULL, VImageRepn, res_image);
for(i = 0; i < n; i++) {
beta_image[i] = VCreateImage(nslices, nrows, ncols, VFloatRepn);
VFillImage(beta_image[i], VAllBands, 0);
VSetAttr(VImageAttrList(beta_image[i]), "patient", NULL, VStringRepn, xinfo->patient);
VSetAttr(VImageAttrList(beta_image[i]), "voxel", NULL, VStringRepn, xinfo->voxel);
VSetAttr(VImageAttrList(beta_image[i]), "repetition_time", NULL, VLongRepn, itr);
VSetAttr(VImageAttrList(beta_image[i]), "talairach", NULL, VStringRepn, xinfo->talairach);
/* neu */
VSetAttr(VImageAttrList(beta_image[i]),"indexOrigin",NULL,VStringRepn,xinfo->indexOrigin);
VSetAttr(VImageAttrList(beta_image[i]),"columnVec",NULL,VStringRepn,xinfo->columnVec);
VSetAttr(VImageAttrList(beta_image[i]),"rowVec",NULL,VStringRepn,xinfo->rowVec);
VSetAttr(VImageAttrList(beta_image[i]),"sliceVec",NULL,VStringRepn,xinfo->sliceVec);
VSetAttr(VImageAttrList(beta_image[i]),"FOV",NULL,VStringRepn,xinfo->FOV);
/*--------*/
if(xinfo->fixpoint[0] != 'N')
VSetAttr(VImageAttrList(beta_image[i]), "fixpoint", NULL, VStringRepn, xinfo->fixpoint);
if(xinfo->ca[0] != 'N') {
VSetAttr(VImageAttrList(beta_image[i]), "ca", NULL, VStringRepn, xinfo->ca);
VSetAttr(VImageAttrList(beta_image[i]), "cp", NULL, VStringRepn, xinfo->cp);
VSetAttr(VImageAttrList(beta_image[i]), "extent", NULL, VStringRepn, xinfo->extent);
}
VSetAttr(VImageAttrList(beta_image[i]), "name", NULL, VStringRepn, "BETA");
VSetAttr(VImageAttrList(beta_image[i]), "modality", NULL, VStringRepn, "BETA");
VSetAttr(VImageAttrList(beta_image[i]), "beta", NULL, VShortRepn, i + 1);
VSetAttr(VImageAttrList(beta_image[i]), "df", NULL, VFloatRepn, df);
VAppendAttr(out_list, "image", NULL, VImageRepn, beta_image[i]);
}
VSetAttr(VImageAttrList(design), "name", NULL, VStringRepn, "X");
VSetAttr(VImageAttrList(design), "modality", NULL, VStringRepn, "X");
VAppendAttr(out_list, "image", NULL, VImageRepn, design);
KX_image = Mat2Vista(SX);
VSetAttr(VImageAttrList(KX_image), "name", NULL, VStringRepn, "KX");
VSetAttr(VImageAttrList(KX_image), "modality", NULL, VStringRepn, "KX");
VAppendAttr(out_list, "image", NULL, VImageRepn, KX_image);
VSetAttr(VImageAttrList(BCOV), "name", NULL, VStringRepn, "BCOV");
VSetAttr(VImageAttrList(BCOV), "modality", NULL, VStringRepn, "BCOV");
VAppendAttr(out_list, "image", NULL, VImageRepn, BCOV);
/*
** create temporary images for smoothness estimation
*/
/* smoothness estim only for 3D images, i.e. CA/CP known */
/* if(xinfo->ca[0] == 'N')
smooth = FALSE;*/
if(smooth) {
i0 = 20;
i1 = i0 + 30;
if(i1 > m)
i1 = m;
for(i = i0; i < i1; i++) {
if(i - i0 >= ETMP)
VError(" too many tmp images");
res_map[i - i0] = VCreateImage(nslices, nrows, ncols, VFloatRepn);
VFillImage(res_map[i - i0], VAllBands, 0);
}
}
/*
** process
*/
ys = gsl_vector_float_alloc(m);
y = gsl_vector_float_alloc(m);
z = gsl_vector_float_alloc(m);
beta = gsl_vector_float_alloc(n);
kernel = GaussKernel((double)sigma);
for(k = 0; k < nlists; k++) {
src[k] = VCreateImage(linfo[k].ntimesteps, nrows, ncols, linfo[k].repn);
VFillImage(src[k], VAllBands, 0);
}
npix = 0;
for(slice = 0; slice < nslices; slice++) {
if(slice % 5 == 0)
fprintf(stderr, " slice: %3d\r", slice);
for(k = 0; k < nlists; k++) {
if(linfo[k].zero[slice] == 0)
goto next1;
fd = open(linfo[k].filename, O_RDONLY);
if(fd == -1)
VError("could not open file %s", linfo[k].filename);
nt = linfo[k].ntimesteps;
if(! VReadBandDataFD(fd, &linfo[k].info[slice], 0, nt, &src[k]))
VError(" error reading data");
close(fd);
}
repn = linfo[0].repn;
for(row = 0; row < nrows; row++) {
for(col = 0; col < ncols; col++) {
for(k = 0; k < nlists; k++)
if(VPixel(src[k], 0, row, col, VShort) < minval + 1)
goto next;
npix++;
/* read time series data */
sum = sum2 = nx = 0;
ptr1 = y->data;
for(k = 0; k < nlists; k++) {
nt = VImageNBands(src[k]);
for(i = 0; i < nt; i++) {
u = VPixel(src[k], i, row, col, VShort);
(*ptr1++) = u;
sum += u;
sum2 += u * u;
nx++;
}
}
mean = sum / nx;
sig = sqrt((double)((sum2 - nx * mean * mean) / (nx - 1.0)));
if(sig < 0.001)
continue;
/* centering and scaling, Seber, p.330 */
ptr1 = y->data;
for(i = 0; i < m; i++) {
u = ((*ptr1) - mean) / sig;
(*ptr1++) = u + 100.0;
}
/* S x y */
ys = VectorConvolve(y, ys, kernel);
/* compute beta's */
fmat_x_vector(XInv, ys, beta);
/* residuals */
fmat_x_vector(SX, beta, z);
err = 0;
ptr1 = ys->data;
ptr2 = z->data;
for(i = 0; i < m; i++) {
d = ((*ptr1++) - (*ptr2++));
err += d * d;
}
/* sigma^2 */
var = err / trace;
/* write residuals output */
VPixel(res_image, slice, row, col, VFloat) = (VFloat)var;
/* save residuals of several timesteps for smoothness estimation */
if(smooth) {
ptr1 = ys->data;
ptr2 = z->data;
err = 0;
for(i = i0; i < i1; i++) {
d = ((*ptr1++) - (*ptr2++));
err += d * d;
VPixel(res_map[i - i0], slice, row, col, VFloat) = d;
}
if (err > 0) err = sqrt(err);
for(i = i0; i < i1; i++) {
d = VPixel(res_map[i - i0], slice, row, col, VFloat);
if (err > 1.0e-6) d /= err;
else d = 0;
VPixel(res_map[i - i0], slice, row, col, VFloat) = d / err;
}
}
/* write beta output */
ptr1 = beta->data;
for(i = 0; i < n; i++)
VPixel(beta_image[i], slice, row, col, VFloat) = (VFloat)(*ptr1++);
next:
;
}
}
next1:
;
}
/*
** Smoothness estimation based on residual images
*/
if(smooth) {
smooth_fwhm = VSmoothnessEstim(res_map, i1 - i0);
sscanf(xinfo->voxel, "%f %f %f", &vx, &vy, &vz);
vx = (vx + vy + vz) / 3.0; /* voxels should be isotropic */
smooth_fwhm *= vx;
fprintf(stderr, " smoothness: %f\n", smooth_fwhm);
VSetAttr(VImageAttrList(res_image), "smoothness", NULL, VFloatRepn, smooth_fwhm);
for(i = 0; i < n; i++) {
VSetAttr(VImageAttrList(beta_image[i]), "smoothness", NULL, VFloatRepn, smooth_fwhm);
}
for(i = 0; i < i1 - i0; i++)
VDestroyImage(res_map[i]);
}
ende:
if(npix == 0)
VError(" no voxels above threshold %d found", minval);
return out_list;
}