int
main(int argc, char *argv[])
{
char **av, *in_vol, *out_vol;
int ac, nargs;
MRI *mri_in, *mri_out, *mri_tmp ;
LTA *lta = 0;
MATRIX *i_to_r_src = 0; /* src geometry of the input LTA */
MATRIX *V_to_V = 0; /* Final voxel-to-voxel transform */
MATRIX *r_to_i_dst = 0; /* dst geometry of the input LTA */
MATRIX *m_tmp = 0;
MATRIX *i_to_r_reg = 0; /* i_to_r of the volume after registration */
MATRIX *r_to_i_out = 0; /* r_to_i of the final output volume */
VOL_GEOM vgm_in;
int x, y, z;
double maxV, minV, value;
// MATRIX *i_to_r, *r_to_i;
/* rkt: check for and handle version tag */
nargs = handle_version_option (argc, argv, "$Id: mri_transform_to_COR.c,v 1.8 2011/03/02 00:04:55 nicks Exp $", "$Name: stable5 $");
if (nargs && argc - nargs == 1)
usage_exit (0);
argc -= nargs;
Progname = argv[0] ;
ErrorInit(NULL, NULL, NULL) ;
DiagInit(NULL, NULL, NULL) ;
ac = argc ;
av = argv ;
for ( ; argc > 1 && ISOPTION(*argv[1]) ; argc--, argv++)
{
nargs = get_option(argc, argv) ;
argc -= nargs ;
argv += nargs ;
}
if (argc < 3)
usage_exit(0) ;
in_vol = argv[1] ;
out_vol = argv[2] ;
printf("reading volume from %s...\n", in_vol) ;
mri_in = MRIread(in_vol) ;
if (!mri_in)
ErrorExit(ERROR_NOFILE, "%s: could not read MRI volume %s", Progname,
in_vol) ;
/* Convert mri_in to float type */
/* double would be more accurate */
if (mri_in->type != MRI_FLOAT)
{
printf("Input volume type is %d\n", mri_in->type);
printf("Change input volume to float type for convenience and accuracy");
mri_tmp = MRIchangeType(mri_in, MRI_FLOAT, 0, 1.0, 1);
MRIfree(&mri_in);
mri_in = mri_tmp; //swap
}
/* Get input volume geometry, which is needed to compute i_to_r
* and r_to_i of input volume. Note that i_to_r and r_to_i assumed
* a certain prespecified c_r, c_a, c_s
*/
getVolGeom(mri_in, &vgm_in);
maxV = -10000.0;
minV = 10000.0;
for (z=0; z < mri_in->depth; z++)
for (y=0; y< mri_in->height; y++)
for (x=0; x < mri_in->width; x++)
{
if (MRIFvox(mri_in, x, y, z) > maxV )
maxV = MRIFvox(mri_in, x, y,z) ;
if (MRIFvox(mri_in, x, y, z) < minV )
minV = MRIFvox(mri_in, x, y,z) ;
}
printf("Input volume has max = %g, min =%g\n", maxV, minV);
printf("Scale input volume by %g \n", scale);
maxV = -10000.0;
minV = 10000.0;
for (z=0; z < mri_in->depth; z++)
for (y=0; y< mri_in->height; y++)
for (x=0; x < mri_in->width; x++)
{
MRIFvox(mri_in, x, y, z) *= scale;
if (MRIFvox(mri_in, x, y, z) > maxV )
maxV = MRIFvox(mri_in, x, y,z) ;
if (MRIFvox(mri_in, x, y, z) < minV )
minV = MRIFvox(mri_in, x, y,z) ;
}
printf("Input volume after scaling has max = %g, min =%g\n", maxV, minV);
/* Try to compute the Voxel_to_Voxel transform from the input volume
* and the registration target/reference volume!
* If no registration is involved, vox_to_vox is simply identity
*/
/* Things become more complicated when allowing inverse transform */
if (transform_flag)
{
int transform_type;
printf("INFO: Applying transformation from file %s...\n",
transform_fname);
transform_type = TransformFileNameType(transform_fname);
/* Read in LTA transform file name */
if (transform_type == MNI_TRANSFORM_TYPE ||
transform_type == TRANSFORM_ARRAY_TYPE ||
transform_type == REGISTER_DAT ||
transform_type == FSLREG_TYPE
)
{
printf("Reading transform ...\n");
lta = LTAreadEx(transform_fname) ;
if (!lta)
ErrorExit(ERROR_NOFILE, "%s: could not read transform file %s",
Progname, transform_fname) ;
if (transform_type == FSLREG_TYPE)
{
if (lta_src == 0 || lta_dst == 0)
{
fprintf(stderr, "ERROR: fslmat does not have information on the src and dst volumes\n");
fprintf(stderr, "ERROR: you must give options '-src' and '-dst' to specify the src and dst volume infos for the registration\n");
}
LTAmodifySrcDstGeom(lta, lta_src, lta_dst); // add src and dst information
//The following is necessary to interpret FSLMAT correctly!!!
LTAchangeType(lta, LINEAR_VOX_TO_VOX);
}
if (lta->xforms[0].src.valid == 0)
{
if (lta_src == 0)
{
fprintf(stderr, "The transform does not have the valid src volume info.\n");
fprintf(stderr, "Either you give src volume info by option -src or\n");
fprintf(stderr, "make the transform to have the valid src info.\n");
ErrorExit(ERROR_BAD_PARM, "Bailing out...\n");
}
else
{
LTAmodifySrcDstGeom(lta, lta_src, NULL); // add src information
}
}
if (lta->xforms[0].dst.valid == 0)
{
if (lta_dst == 0)
{
fprintf(stderr, "The transform does not have the valid dst volume info.\n");
fprintf(stderr, "Either you give src volume info by option -dst or\n");
fprintf(stderr, "make the transform to have the valid dst info.\n");
fprintf(stderr, "If the dst was average_305, then you can set\n");
fprintf(stderr, "environmental variable USE_AVERAGE305 true\n");
fprintf(stderr, "instead.\n");
ErrorExit(ERROR_BAD_PARM, "Bailing out...\n");
}
else
{
LTAmodifySrcDstGeom(lta, NULL, lta_dst); // add dst information
}
}
// The following procedure aims to apply an LTA computed from COR format to a volume in non-COR format, or vice versa, as long as they share the same RAS
// first change to LINEAR RAS_TO_RAS using old info
if (lta->type != LINEAR_RAS_TO_RAS)
{
LTAchangeType(lta, LINEAR_RAS_TO_RAS);
}
// now possiblly reset the src and dst
if (lta_src != NULL)
{
//always trust the user
LTAmodifySrcDstGeom(lta, lta_src, NULL);
}
if (lta_dst != NULL)
{
//always trust the user
LTAmodifySrcDstGeom(lta, NULL, lta_dst);
}
if (lta->type == LINEAR_RAS_TO_RAS)
{
/* Convert it to VOX_TO_VOX */
/* VOXELsrc_to_VOXELdst = R2Vdst*R2Rlta*V2Rsrc */
/* Note whether the input should be identical to src or dst here depends
* on whether the LTA here is the direct or inverse transform
*/
i_to_r_src = vg_i_to_r(<a->xforms[0].src);
r_to_i_dst = vg_r_to_i(<a->xforms[0].dst);
if (!r_to_i_dst || !i_to_r_src)
ErrorExit(ERROR_BADFILE, "%s: failed to extract volume geometries from input LTA file",Progname);
m_tmp = MatrixMultiply(lta->xforms[0].m_L, i_to_r_src, NULL);
V_to_V = MatrixMultiply(r_to_i_dst, m_tmp, NULL);
MatrixFree(&m_tmp);
MatrixFree(&i_to_r_src);
MatrixFree(&r_to_i_dst);
}
}
else
{
fprintf(stderr, "unknown transform type in file %s\n",
transform_fname);
exit(1);
}
if (invert_flag)
{
/* Geometry of input volume should match that of the dst of the LTA */
if (MYvg_isEqual(<a->xforms[0].dst, &vgm_in) == 0)
{
ErrorExit(ERROR_BADFILE, "%s: dst volume of lta doesn't match that of input volume",Progname);
}
i_to_r_reg = vg_i_to_r(<a->xforms[0].src);
if (!i_to_r_reg)
ErrorExit(ERROR_BADFILE, "%s: failed to extract i_to_r of registered volume from LTA",Progname);
m_tmp = MatrixInverse(V_to_V, NULL);
if (!m_tmp)
ErrorExit(ERROR_BADPARM, "%s: transform is singular!", Progname);
MatrixFree(&V_to_V);
V_to_V = m_tmp;
}
else
{
/* Geometry of input volume should match that of the src of the LTA */
if (MYvg_isEqual(<a->xforms[0].src, &vgm_in) == 0)
{
ErrorExit(ERROR_BADFILE, "%s: src volume of lta doesn't match that of input volume",Progname);
}
i_to_r_reg = vg_i_to_r(<a->xforms[0].dst);
if (!i_to_r_reg)
ErrorExit(ERROR_BADFILE, "%s: failed to extract i_to_r of registered volume from LTA",Progname);
}
}
else
{
/* No registration transform need be applied */
V_to_V = MatrixIdentity(4, NULL);
i_to_r_reg = extract_i_to_r(mri_in);
if (!i_to_r_reg)
ErrorExit(ERROR_BADFILE, "%s: failed to extract i_to_r from input volume",Progname);
}
/* Now need to find the vox-to-vox transformation between registered volume
* (or input volume itself if no registration involved) and the output
* volume, either in COR format or as the out-like volume
*/
/* Given a volume with a certain i_to_r, we need to compute the necessary
* vox-to-voxel transform to change its i_to_r to like another volume.
* The vox-to-vox is equal to R2V(r_to_i)_likevol*i_to_r_current_vol.
*/
if (out_like_fname)
{
mri_tmp = MRIread(out_like_fname) ;
if (!mri_tmp)
ErrorExit(ERROR_NOFILE, "%s: could not read template volume from %s",out_like_fname) ;
/* out_type = mri_tmp->type; */
/* specify the out-type to float initially so as not to lose accuracy
* during reslicing, will change type to correct type later.
*/
mri_out = MRIalloc(mri_tmp->width, mri_tmp->height, mri_tmp->depth, MRI_FLOAT) ;
MRIcopyHeader(mri_tmp, mri_out) ;
MRIfree(&mri_tmp);
}
else /* assume output is in COR format */
{
mri_out = MRIalloc(256, 256, 256, MRI_FLOAT) ;
/* out_type = MRI_UCHAR; */
/* Who says MRIlinearTransformInterp will change the header??
* I don't think so!
*/
//E/ set xyzc_ras to coronal ones.. - these'll get zorched
//by MRIlinearTransformInterp() - copy again later - is there
//any use in having them here now? yes, so we can pass mri_out
//to the ras2vox fns.
mri_out->imnr0 = 1; /* what's this? */
mri_out->imnr1 = 256; /* what's this? */
mri_out->thick = 1.0;
mri_out->ps = 1.0; /* what's this? */
mri_out->xsize = mri_out->ysize = mri_out->zsize = 1.0;
mri_out->xstart = mri_out->ystart = mri_out->zstart = -128.0;
mri_out->xend = mri_out->yend = mri_out->zend = 128.0;
mri_out->x_r =-1;
mri_out->y_r = 0;
mri_out->z_r = 0;
mri_out->x_a = 0;
mri_out->y_a = 0;
mri_out->z_a = 1;
mri_out->x_s = 0;
mri_out->y_s =-1;
mri_out->z_s = 0;
/* In this case, the RAS itself is not fully determined, i.e., c_ras.
* It's quite arbitrary, different values just change the final
* sitting of the volume inside the RAS system.
*/
/* NO! The C_RAS has to be set correctly, depending which target
* volume the previous Vox_to_Vox transformation assumes!
* When a registration is involved, the target volume is either
* the src of LTA (direct) or the dst (inverse transform). When
* just change format, the target volume is the input itself!!
*/
if (transform_flag)
{
if (invert_flag)
{
mri_out->c_r = lta->xforms[0].src.c_r;
mri_out->c_a = lta->xforms[0].src.c_a;
mri_out->c_s = lta->xforms[0].src.c_s;
}
else
{
mri_out->c_r = lta->xforms[0].dst.c_r;
mri_out->c_a = lta->xforms[0].dst.c_a;
mri_out->c_s = lta->xforms[0].dst.c_s;
}
}
else
{
mri_out->c_r = mri_in->c_r;
mri_out->c_a = mri_in->c_a;
mri_out->c_s = mri_in->c_s;
}
mri_out->ras_good_flag=1; /* What does this flag mean ? */
/* since output is just transformed input */
MRIcopyPulseParameters(mri_in, mri_out) ;
}
/* Compute the final input-to-output VOX_to_VOX transformation matrix */
r_to_i_out = extract_r_to_i(mri_out);
m_tmp = MatrixMultiply(r_to_i_out, i_to_r_reg, NULL);
V_to_V = MatrixMultiply(m_tmp, V_to_V, V_to_V);
MatrixFree(&m_tmp);
printf("InterpMethod = %d\n", InterpMethod);
/* Modify the MyMRIlinearTr... if I want to implement my cubic-B-spline
* interpolation method. Otherwise, unnecessary
*/
/* mri_out = MyMRIlinearTransformInterp(mri_in, mri_out, V_to_V, InterpMethod); */
if (InterpMethod == SAMPLE_BSPLINE)
mri_out = MRIlinearTransformInterpBSpline(mri_in, mri_out, V_to_V,
SplineDegree);
else
mri_out = MRIlinearTransformInterp(mri_in, mri_out, V_to_V, InterpMethod);
maxV = -10000.0;
minV = 10000.0;
for (z=0; z < mri_out->depth; z++)
for (y=0; y< mri_out->height; y++)
for (x=0; x < mri_out->width; x++)
{
if (MRIFvox(mri_out, x, y, z) > maxV )
maxV = MRIFvox(mri_out, x, y,z) ;
if (MRIFvox(mri_out, x, y, z) < minV )
minV = MRIFvox(mri_out, x, y,z) ;
}
if (autoscale)
{
noscale = 1;
/* compute histogram of output volume */
HISTOGRAM *h, *hsmooth ;
float fmin, fmax, val, peak, smooth_peak;
int i, nbins, bin;
fmin = minV;
fmax = maxV;
if (fmin < 0) fmin = 0;
nbins = 256 ;
h = HISTOalloc(nbins) ;
hsmooth = HISTOcopy(h, NULL) ;
HISTOclear(h, h) ;
h->bin_size = (fmax-fmin)/255.0 ;
for (i = 0 ; i < nbins ; i++)
h->bins[i] = (i+1)*h->bin_size ;
for (z=0; z < mri_out->depth; z++)
for (y=0; y< mri_out->height; y++)
for (x=0; x < mri_out->width; x++)
{
val = MRIFvox(mri_out, x, y, z);
if (val <= 0) continue;
bin = nint((val - fmin)/h->bin_size);
if (bin >= h->nbins)
bin = h->nbins-1;
else if (bin < 0)
bin = 0;
h->counts[bin] += 1.0;
}
HISTOfillHoles(h) ;
HISTOsmooth(h, hsmooth, 5) ;
peak =
hsmooth->bins[HISTOfindHighestPeakInRegion(h, 1, h->nbins)] ;
// smooth_peak =
// hsmooth->bins[HISTOfindHighestPeakInRegion(hsmooth, 1, hsmooth->nbins)] ;
smooth_peak =
hsmooth->bins[HISTOfindLastPeak(hsmooth, 5, 0.8)] ;
/*
bin = nint((smooth_peak - fmin)/hsmooth->bin_size) ;
printf("Highest peak has count = %d\n", (int)hsmooth->counts[bin]);
bin = nint((420 - fmin)/hsmooth->bin_size) ;
printf("bin at 420 has count = %d\n", (int)hsmooth->counts[bin]);
*/
scale = 110.0/smooth_peak;
printf("peak of output volume is %g, smooth-peak is %g, multiply by %g to scale it to 110\n", peak, smooth_peak, scale);
for (z=0; z < mri_out->depth; z++)
for (y=0; y< mri_out->height; y++)
for (x=0; x < mri_out->width; x++)
{
val = MRIFvox(mri_out, x, y, z);
MRIFvox(mri_out, x, y, z) = val*scale;
}
}
printf("Output volume (before type-conversion) has max = %g, min =%g\n", maxV, minV);
/* Finally change type to desired */
if (mri_out->type != out_type)
{
printf("Change output volume to type %d\n", out_type);
/* I need to modify the MIRchangeType function to make sure
* it does roundoff instead of simple truncation!
*/
/* Note if the last flag is set to 1, then it won't do scaling
and small float numbers will become zero after convert to
BYTE
*/
if (out_type == 0 && noscale == 1)
{
//convert data to UCHAR
mri_tmp = MRIalloc(mri_out->width, mri_out->height, mri_out->depth, out_type) ;
MRIcopyHeader(mri_out, mri_tmp);
for (z=0; z < mri_out->depth; z++)
for (y=0; y< mri_out->height; y++)
for (x=0; x < mri_out->width; x++)
{
value = floor(MRIgetVoxVal(mri_out, x, y, z, 0) + 0.5);
if (value < 0 ) value = 0;
if (value > 255) value = 255;
MRIvox(mri_tmp,x,y,z) = (unsigned char)value;
}
}
else
mri_tmp = MRIchangeType(mri_out, out_type, thred_low, thred_high, noscale);
MRIfree(&mri_out);
mri_out = mri_tmp; //swap
}
MRIwrite(mri_out, out_vol) ;
MRIfree(&mri_in);
MRIfree(&mri_out);
if (lta_src)
MRIfree(<a_src);
if (lta_dst)
MRIfree(<a_dst);
MatrixFree(&V_to_V);
if (!r_to_i_out)
MatrixFree(&r_to_i_out);
if (!i_to_r_reg)
MatrixFree(&i_to_r_reg);
return(0) ; /* for ansi */
}
static MRI *
MRIremoveWMOutliersAndRetainMedialSurface(MRI *mri_src,
MRI *mri_src_ctrl,
MRI *mri_dst_ctrl,
int intensity_below)
{
MRI *mri_inside, *mri_bin ;
HISTOGRAM *histo, *hsmooth ;
int wm_peak, x, y, z, nremoved ;
float thresh, hi_thresh ;
double val, lmean, max ;
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON)
{
MRIwrite(mri_src_ctrl, "sc.mgz") ;
}
if (mri_dst_ctrl != mri_src_ctrl)
{
mri_dst_ctrl = MRIcopy(mri_src_ctrl, mri_dst_ctrl) ;
}
mri_inside = MRIerode(mri_dst_ctrl, NULL) ;
MRIbinarize(mri_inside, mri_inside, 1, 0, 1) ;
histo = MRIhistogramLabel(mri_src, mri_inside, 1, 256) ;
hsmooth = HISTOcopy(histo, NULL) ;
HISTOsmooth(histo, hsmooth, 2) ;
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON)
{
HISTOplot(histo, "h.plt") ;
HISTOplot(hsmooth, "hs.plt") ;
}
printf("using wm (%d) threshold %2.1f for removing exterior voxels\n",
wm_peak, thresh) ;
wm_peak = HISTOfindHighestPeakInRegion(hsmooth, 1, hsmooth->nbins-1) ;
wm_peak = hsmooth->bins[wm_peak] ;
thresh = wm_peak-intensity_below ;
hi_thresh = wm_peak-.5*intensity_below ;
printf("using wm (%d) threshold %2.1f for removing exterior voxels\n",
wm_peak, thresh) ;
// now remove stuff that's on the border and is pretty dark
for (nremoved = x = 0 ; x < mri_src->width ; x++)
{
for (y = 0 ; y < mri_src->height ; y++)
{
for (z = 0 ; z < mri_src->depth ; z++)
{
if (x == Gx && y == Gy && z == Gz)
{
DiagBreak() ;
}
/* if it's a control point,
it's not in the interior of the wm,
and it's T1 val is too low */
if (MRIgetVoxVal(mri_dst_ctrl, x, y, z, 0) == 0)
{
continue ; // not a control point
}
/* if it's way far from the wm mode
then remove it even if it's in the interior */
val = MRIgetVoxVal(mri_src, x, y, z, 0) ;
if (val < thresh-5)
{
MRIsetVoxVal(mri_dst_ctrl, x, y, z, 0, 0) ;
nremoved++ ;
}
if (nint(MRIgetVoxVal(mri_inside, x, y, z, 0)) > 0)
// don't process interior voxels further
{
continue ; // in the interior
}
if (val < thresh)
{
MRIsetVoxVal(mri_dst_ctrl, x, y, z, 0, 0) ;
nremoved++ ;
}
else
{
lmean =
MRImeanInLabelInRegion(mri_src, mri_inside, 1, x, y, z, 7);
if (val < lmean-10)
{
MRIsetVoxVal(mri_dst_ctrl, x, y, z, 0, 0) ;
nremoved++ ;
}
}
}
}
}
#if 0
for (x = 0 ; x < mri_src->width ; x++)
{
for (y = 0 ; y < mri_src->height ; y++)
{
for (z = 0 ; z < mri_src->depth ; z++)
{
if (x == Gx && y == Gy && z == Gz)
{
DiagBreak() ;
}
/* if it's a control point,
it's not in the interior of the wm,
and it's T1 val is too low */
if (MRIgetVoxVal(mri_dst_ctrl, x, y, z, 0) == 0)
{
continue ; // not a control point
}
if (MRIcountNonzeroInNbhd(mri_dst_ctrl,3, x, y, z)<=2)
{
MRIsetVoxVal(mri_dst_ctrl, x, y, z, 0, 0) ;
nremoved++ ;
}
}
}
}
#endif
/* now take out voxels that have too big an intensity diff
with surrounding ones */
mri_bin = MRIbinarize(mri_dst_ctrl, NULL, 1, 0, 1) ;
for (x = 0 ; x < mri_src->width ; x++)
{
for (y = 0 ; y < mri_src->height ; y++)
{
for (z = 0 ; z < mri_src->depth ; z++)
{
if (x == Gx && y == Gy && z == Gz)
{
DiagBreak() ;
}
/* if it's a control point,
it's not in the interior of the wm,
and it's T1 val is too low */
if (MRIgetVoxVal(mri_dst_ctrl, x, y, z, 0) == 0)
{
continue ; // not a control point
}
val = MRIgetVoxVal(mri_src, x, y, z, 0) ;
max = MRImaxInLabelInRegion(mri_src, mri_bin, 1, x, y, z, 3);
if (val+7 < max && val < hi_thresh)
{
MRIsetVoxVal(mri_dst_ctrl, x, y, z, 0, 0) ;
nremoved++ ;
}
}
}
}
MRIfree(&mri_bin) ;
printf( "%d control points removed\n", nremoved) ;
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON)
{
MRIwrite(mri_dst_ctrl, "dc.mgz") ;
}
HISTOfree(&histo) ;
HISTOfree(&hsmooth) ;
MRIfree(&mri_inside) ;
return(mri_dst_ctrl) ;
}
static int
remove_surface_outliers(MRI *mri_ctrl_src, MRI *mri_dist, MRI *mri_src,
MRI *mri_ctrl_dst)
{
int x, y, z, wsize ;
HISTOGRAM *h, *hs ;
double mean, sigma, val ;
MRI *mri_outlier = MRIclone(mri_ctrl_src, NULL) ;
mri_ctrl_dst = MRIcopy(mri_ctrl_src, mri_ctrl_dst) ;
wsize = nint(WSIZE_MM/mri_src->xsize) ;
for (x = 0 ; x < mri_src->width ; x++)
for (y = 0 ; y < mri_src->height ; y++)
for (z = 0 ; z < mri_src->depth ; z++)
{
if (x == Gx && y == Gy && z == Gz)
{
DiagBreak() ;
}
if ((int)MRIgetVoxVal(mri_ctrl_src, x, y,z, 0) == 0)
{
continue ; // not a control point
}
val = MRIgetVoxVal(mri_src, x, y, z, 0) ;
#if 1
if (val < 80 || val > 130)
{
MRIsetVoxVal(mri_ctrl_dst, x, y, z, 0, 0) ; // remove it as a control point
MRIsetVoxVal(mri_outlier, x, y, z, 0, 1) ; // diagnostics
continue ;
}
#endif
#if 1
if (val > 100 || val < 120)
{
continue ; // not an outlier
}
#endif
h = MRIhistogramVoxel(mri_src, 0, NULL, x, y, z,
wsize, mri_dist, mri_src->xsize) ;
HISTOsoapBubbleZeros(h, h, 100) ;
hs = HISTOsmooth(h, NULL, .5);
HISTOrobustGaussianFit(hs, .5, &mean, &sigma) ;
#define MAX_SIGMA 10 // for intensity normalized images
if (sigma > MAX_SIGMA)
{
sigma = MAX_SIGMA ;
}
if (fabs((mean-val)/sigma) > 2)
{
MRIsetVoxVal(mri_ctrl_dst, x, y, z, 0, 0) ; // remove it as a control point
MRIsetVoxVal(mri_outlier, x, y, z, 0, 1) ; // diagnostics
}
if (Gdiag & DIAG_WRITE)
{
HISTOplot(h, "h.plt") ;
HISTOplot(h, "hs.plt") ;
}
HISTOfree(&h) ;
HISTOfree(&hs) ;
}
if (Gdiag & DIAG_WRITE)
{
MRIwrite(mri_outlier, "o.mgz") ;
}
MRIfree(&mri_outlier) ;
return(NO_ERROR) ;
}
static MRI *
MRIremoveWMOutliers(MRI *mri_src, MRI *mri_src_ctrl, MRI *mri_dst_ctrl,
int intensity_below)
{
MRI *mri_bin, *mri_outliers = NULL ;
float max, thresh, val;
HISTOGRAM *histo, *hsmooth ;
int wm_peak, x, y, z, nremoved = 0, whalf = 5, total ;
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON)
{
MRIwrite(mri_src_ctrl, "sc.mgz") ;
}
if (mri_dst_ctrl == NULL)
{
mri_dst_ctrl = MRIcopy(mri_src_ctrl, NULL) ;
}
mri_bin = MRIbinarize(mri_src_ctrl, NULL, 1, 0, CONTROL_MARKED) ;
histo = MRIhistogramLabel(mri_src, mri_bin, 1, 256) ;
hsmooth = HISTOcopy(histo, NULL) ;
HISTOsmooth(histo, hsmooth, 2) ;
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON)
{
HISTOplot(histo, "h.plt") ;
HISTOplot(hsmooth, "hs.plt") ;
}
wm_peak = HISTOfindHighestPeakInRegion(hsmooth, 1, hsmooth->nbins-1) ;
wm_peak = hsmooth->bins[wm_peak] ;
thresh = wm_peak-intensity_below ;
HISTOfree(&histo) ;
HISTOfree(&hsmooth) ;
if (Gdiag & DIAG_WRITE)
{
mri_outliers = MRIclone(mri_dst_ctrl, NULL) ;
}
for (total = x = 0 ; x < mri_src->width ; x++)
{
for (y = 0 ; y < mri_src->height ; y++)
{
for (z = 0 ; z < mri_src->depth ; z++)
{
if (x == Gx && y == Gy && z == Gz)
{
DiagBreak() ;
}
if (nint(MRIgetVoxVal(mri_dst_ctrl, x, y, z, 0)) == 0)
{
continue ;
}
max = MRImaxInLabelInRegion(mri_src, mri_bin, 1, x, y, z, whalf);
val = MRIgetVoxVal(mri_src, x, y, z, 0) ;
total++ ;
if (val+intensity_below < max && val < thresh)
{
MRIsetVoxVal(mri_dst_ctrl, x, y, z, 0, 0) ;
if (mri_outliers)
{
MRIsetVoxVal(mri_outliers, x, y, z, 0, 128) ;
}
nremoved++ ;
}
}
}
}
printf( "%d control points removed (%2.1f%%)\n",
nremoved, 100.0*(double)nremoved/(double)total) ;
if (mri_outliers)
{
printf( "writing out.mgz outlier volume\n") ;
MRIwrite(mri_outliers, "out.mgz") ;
MRIfree(&mri_outliers) ;
}
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON)
{
MRIwrite(mri_dst_ctrl, "dc.mgz") ;
}
MRIfree(&mri_bin) ;
return(mri_dst_ctrl);
}
static MRI *
MRIremoveWMOutliersAndRetainMedialSurface(MRI *mri_src,
MRI *mri_src_ctrl,
MRI *mri_dst_ctrl,
int intensity_below)
{
MRI *mri_bin, *mri_dist, *mri_dist_sup, *mri_outliers = NULL ;
float max, thresh, val;
HISTOGRAM *histo, *hsmooth ;
int wm_peak, x, y, z, nremoved = 0, whalf = 5 ;
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON)
{
MRIwrite(mri_src_ctrl, "sc.mgz") ;
}
mri_bin = MRIbinarize(mri_dst_ctrl, NULL, 1, 0, 1) ;
mri_dist = MRIdistanceTransform(mri_bin, NULL, 1, -1,
DTRANS_MODE_SIGNED, NULL);
MRIscalarMul(mri_dist, mri_dist, -1) ;
mri_dist_sup = MRInonMaxSuppress(mri_dist, NULL, 0, 1) ;
mri_dst_ctrl = MRIbinarize(mri_dist_sup, mri_dst_ctrl, 1, 0, 1) ;
histo = MRIhistogramLabel(mri_src, mri_src_ctrl, 1, 256) ;
hsmooth = HISTOcopy(histo, NULL) ;
HISTOsmooth(histo, hsmooth, 2) ;
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON)
{
HISTOplot(histo, "h.plt") ;
HISTOplot(hsmooth, "hs.plt") ;
}
wm_peak = HISTOfindHighestPeakInRegion(hsmooth, 1, hsmooth->nbins-1) ;
wm_peak = hsmooth->bins[wm_peak] ;
thresh = wm_peak-intensity_below ;
HISTOfree(&histo) ;
HISTOfree(&hsmooth) ;
if (Gdiag & DIAG_WRITE)
{
mri_outliers = MRIclone(mri_dst_ctrl, NULL) ;
}
for (x = 0 ; x < mri_src->width ; x++)
{
for (y = 0 ; y < mri_src->height ; y++)
{
for (z = 0 ; z < mri_src->depth ; z++)
{
if (x == Gx && y == Gy && z == Gz)
{
DiagBreak() ;
}
if (nint(MRIgetVoxVal(mri_dst_ctrl, x, y, z, 0)) == 0)
{
continue ;
}
max = MRImaxInLabelInRegion(mri_src, mri_dst_ctrl, 1, x, y, z, whalf);
val = MRIgetVoxVal(mri_src, x, y, z, 0) ;
if (val+intensity_below < max && val < thresh)
{
MRIsetVoxVal(mri_dst_ctrl, x, y, z, 0, 0) ;
if (mri_outliers)
{
MRIsetVoxVal(mri_outliers, x, y, z, 0, 128) ;
}
nremoved++ ;
}
}
}
}
printf( "%d control points removed\n", nremoved) ;
if (mri_outliers)
{
printf( "writing out.mgz outlier volume\n") ;
MRIwrite(mri_outliers, "out.mgz") ;
MRIfree(&mri_outliers) ;
}
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON)
{
MRIwrite(mri_dst_ctrl, "dc.mgz") ;
}
MRIfree(&mri_bin) ;
MRIfree(&mri_dist);
MRIfree(&mri_dist_sup);
return(mri_dst_ctrl);
}
static GCA_SAMPLE *
find_control_points(GCA *gca, GCA_SAMPLE *gcas_total,
int total_samples, int *pnorm_samples, int nregions, int label,
MRI *mri_in, TRANSFORM *transform, double min_prior, double ctl_point_pct)
{
int i, j, *ordered_indices, nsamples,
xmin, ymin, zmin, xmax, ymax, zmax, xv,yv,zv,
x, y, z, xi, yi, zi, region_samples,
used_in_region, prior_wsize=5, image_wsize=3, histo_peak, n,
nbins ;
GCA_SAMPLE *gcas, *gcas_region, *gcas_norm ;
double means[MAX_GCA_INPUTS], vars[MAX_GCA_INPUTS], val, nsigma ;
HISTOGRAM *histo, *hsmooth ;
GC1D *gc ;
float fmin, fmax ;
MRI *mri_T1 = NULL ;
if (label == Gdiag_no)
DiagBreak() ;
MRIvalRange(mri_in, &fmin, &fmax) ;
nbins = (int)(fmax-fmin+1);
histo = HISTOalloc(nbins) ; hsmooth = HISTOalloc(nbins) ;
for (nsamples = i = 0 ; i < total_samples ; i++)
{
if (gcas_total[i].label != label)
continue ;
nsamples++ ;
}
*pnorm_samples = 0 ;
printf("found %d control points for structure...\n", nsamples) ;
if (nsamples == 0)
{
DiagBreak() ;
return(NO_ERROR) ;
}
gcas = (GCA_SAMPLE *)calloc(nsamples, sizeof(GCA_SAMPLE)) ;
gcas_region = (GCA_SAMPLE *)calloc(nsamples, sizeof(GCA_SAMPLE)) ;
gcas_norm = (GCA_SAMPLE *)calloc(nsamples, sizeof(GCA_SAMPLE)) ;
if (!gcas || !gcas_region || !gcas_norm)
ErrorExit
(ERROR_NOMEMORY,
"find_control_points: could not allocate %d samples\n",nsamples);
for (j = i = 0 ; i < total_samples ; i++)
{
if (gcas_total[i].label != label)
continue ;
memmove(&gcas[j], &gcas_total[i], sizeof(GCA_SAMPLE)) ;
j++ ;
}
ordered_indices = (int *)calloc(nsamples, sizeof(int)) ;
gcas_bounding_box(gcas, nsamples, &xmin, &ymin, &zmin, &xmax, &ymax, &zmax, label) ;
printf("bounding box (%d, %d, %d) --> (%d, %d, %d)\n",
xmin, ymin, zmin, xmax, ymax, zmax) ;
for (x = 0 ; x < nregions ; x++)
{
for (y = 0 ; y < nregions ; y++)
{
for (z = 0 ; z < nregions ; z++)
{
/* only process samples in this region */
nsigma = 1.0 ;
do
{
for (region_samples = i = 0 ; i < nsamples ; i++)
{
xi = (int)(nregions*(gcas[i].x - xmin) / (xmax-xmin+1)) ;
yi = (int)(nregions*(gcas[i].y - ymin) / (ymax-ymin+1)) ;
zi = (int)(nregions*(gcas[i].z - zmin) / (zmax-zmin+1)) ;
if ((xi < 0 || xi >= nregions) ||
(yi < 0 || yi >= nregions) ||
(zi < 0 || zi >= nregions))
DiagBreak() ;
xv = gcas[i].x ; yv = gcas[i].y ; zv = gcas[i].z ;
if (xi != x || yi != y || zi != z
|| gcas[i].prior < min_prior)
continue ;
if (xv == Gx && yv == Gy && zv == Gz)
DiagBreak() ;
if (sqrt(SQR(xv-Gx)+SQR(yv-Gy)+SQR(zv-Gz)) < 2)
DiagBreak() ;
if (min_region_prior(gca, gcas[i].xp, gcas[i].yp, gcas[i].zp,prior_wsize, label) < min_prior)
continue ;
if (uniform_region(gca, mri_in, transform,
xv, yv, zv,
image_wsize, &gcas[i], nsigma) == 0)
continue ;
memmove(&gcas_region[region_samples],
&gcas[i],
sizeof(GCA_SAMPLE)) ;
region_samples++ ;
if (gcas[i].x == Gx &&
gcas[i].y == Gy &&
gcas[i].z == Gz)
DiagBreak() ;
}
nsigma *= 1.1 ;
} while (region_samples < 8 && nsigma < 3) ;
if (region_samples < 8)/* can't reliably estimate statistics */
continue ;
if (DIAG_VERBOSE_ON)
printf("\t%d total samples found in region (%d, %d, %d)\n",
region_samples,x, y,z) ;
/* compute mean and variance of label within this region */
for (n = 0 ; n < mri_in->nframes ; n++)
{
HISTOclear(histo, histo) ;
histo->bin_size = 1 ;
for (means[n] = vars[n] = 0.0, i = 0 ;
i < region_samples ;
i++)
{
MRIsampleVolumeFrame
(mri_in,
gcas_region[i].x,gcas_region[i].y,gcas_region[i].z,
n, &val) ;
if (FZERO(val))
{
if (i < (region_samples-1))
memmove(&gcas_region[i],
&gcas_region[i+1],
(region_samples-(i+1))*sizeof(GCA_SAMPLE));
i-- ;
region_samples-- ;
continue ;
}
histo->counts[(int)val]++ ;
means[n] += val ;
vars[n] += (val*val) ;
}
HISTOsmooth(histo, hsmooth, 2) ;
histo_peak =
HISTOfindHighestPeakInRegion(hsmooth, 1, hsmooth->nbins) ;
if (histo_peak < 0) /* couldn't find a valid peak? */
break ;
for (means[n] = vars[n] = 0.0, i = 0 ;
i < region_samples ;
i++)
{
if (gcas_region[i].label < 0)
continue ;
MRIsampleVolumeFrame
(mri_in,
gcas_region[i].x,
gcas_region[i].y,
gcas_region[i].z,
n, &val) ;
means[n] += val ;
vars[n] += (val*val) ;
}
means[n] /= (double)region_samples ;
vars[n] = vars[n] / (double)region_samples - means[n]*means[n] ;
means[n] = histo_peak ;
if (DIAG_VERBOSE_ON)
printf("\tlabel %s[%d]: %2.1f +- %2.1f\n",
cma_label_to_name(label),
n, means[n], sqrt(vars[n])) ;
}
/* ignore GCA mean and variance -
use image instead (otherwise bias field will mess us up) */
for (i = 0 ; i < region_samples ; i++)
{
int r ;
for (r = 0 ; r < gca->ninputs ; r++)
gcas_region[i].means[r] = means[r] ;
/* gcas_region[i].var = var ;*/
}
GCAcomputeLogSampleProbability
(gca, gcas_region, mri_in, transform, region_samples) ;
GCArankSamples
(gca, gcas_region, region_samples, ordered_indices) ;
GCAremoveOutlyingSamples
(gca, gcas_region, mri_in, transform, region_samples, 2.0) ;
for (used_in_region = i = 0 ; i < region_samples ; i++)
{
j = ordered_indices[i] ;
if (gcas_region[j].label != label) /* it was an outlier */
continue ;
memmove
(&gcas_norm[*pnorm_samples],
&gcas_region[j],
sizeof(GCA_SAMPLE)) ;
(*pnorm_samples)++ ; used_in_region++ ;
}
if ((used_in_region <= 0) && region_samples>0)
{
j = ordered_indices[0] ;
/* gcas_region[j].label = label ;*/
printf("forcing use of sample %d @ (%d, %d, %d)\n", j,
gcas_region[j].x,
gcas_region[j].y,
gcas_region[j].z) ;
memmove(&gcas_norm[*pnorm_samples],
&gcas_region[j],
sizeof(GCA_SAMPLE)) ;
(*pnorm_samples)++ ; used_in_region++ ;
}
if (DIAG_VERBOSE_ON)
printf("\t%d samples used in region\n", used_in_region) ;
}
}
}
/* put gca means back into samples */
for (i = 0 ; i < *pnorm_samples ; i++)
{
gc = GCAfindPriorGC(gca,
gcas_norm[i].xp,
gcas_norm[i].yp,
gcas_norm[i].zp,
gcas_norm[i].label) ;
if (gc)
{
int r, c, v ;
for (v = r = 0 ; r < gca->ninputs ; r++)
{
for (c = r ; c < gca->ninputs ; c++, v++)
{
gcas_norm[i].means[v] = gc->means[v] ;
gcas_norm[i].covars[v] = gc->covars[v] ;
}
}
}
}
HISTOfree(&histo) ; HISTOfree(&hsmooth) ;
free(gcas_region) ;
free(gcas) ;
if (mri_T1)
MRIfree(&mri_T1) ;
return(gcas_norm) ;
}
static int
discard_unlikely_control_points(GCA *gca, GCA_SAMPLE *gcas, int nsamples,
MRI *mri_in, TRANSFORM *transform, char *name)
{
int i, xv, yv, zv, n, peak, start, end, num ;
HISTO *h, *hsmooth ;
float fmin, fmax ;
Real val, mean_ratio ;
if (nsamples == 0)
return(NO_ERROR) ;
for (num = n = 0 ; n < mri_in->nframes ; n++)
{
int niter = 0 ;
MRIvalRangeFrame(mri_in, &fmin, &fmax, n) ;
h = HISTOalloc(nint(fmax-fmin)+1) ;
h->bin_size = (fmax-fmin)/(float)h->nbins ;
for (i = 0 ; i < h->nbins ; i++)
h->bins[i] = (i+1)*h->bin_size+fmin ;
for (i = 0 ; i < nsamples ; i++)
{
xv = gcas[i].x ; yv = gcas[i].y ; zv = gcas[i].z ;
if (xv == Gx && yv == Gy && zv == Gz)
DiagBreak() ;
MRIsampleVolumeFrame(mri_in, gcas[i].x,gcas[i].y,gcas[i].z, n, &val) ;
if (FZERO(val))
DiagBreak() ;
h->counts[nint(val-fmin)]++ ;
}
/* check to see if peak is unlikely */
hsmooth = HISTOsmooth(h, NULL, 2) ;
do
{
if (gca->ninputs == 1) /* find brightest peak as
for n=1 it is T1 weighted */
peak = HISTOfindLastPeak(hsmooth, HISTO_WINDOW_SIZE,MIN_HISTO_PCT);
else
peak = HISTOfindHighestPeakInRegion(hsmooth, 0, h->nbins-1) ;
end = HISTOfindEndOfPeak(hsmooth, peak, 0.01) ;
start = HISTOfindStartOfPeak(hsmooth, peak, 0.01) ;
for (mean_ratio = 0.0, i = 0 ; i < nsamples ; i++)
{
mean_ratio += hsmooth->bins[peak] / gcas[i].means[0];
}
mean_ratio /= (Real)nsamples ;
HISTOclearBins
(hsmooth, hsmooth, hsmooth->bins[start], hsmooth->bins[end]) ;
if (niter++ > 5)
break ;
if (niter > 1)
DiagBreak() ;
} while (mean_ratio < 0.5 || mean_ratio > 2.0) ;
printf("%s: limiting intensities to %2.1f --> %2.1f\n",
name, fmin+start, fmin+end) ;
for (i = 0 ; i < nsamples ; i++)
{
xv = gcas[i].x ; yv = gcas[i].y ; zv = gcas[i].z ;
if (xv == Gx && yv == Gy && zv == Gz)
DiagBreak() ;
MRIsampleVolumeFrame(mri_in,gcas[i].x,gcas[i].y,gcas[i].z,n,&val) ;
if (val-fmin < start || val-fmin > end)
{
num++ ; gcas[i].label = 0 ;
}
}
HISTOfree(&h) ; HISTOfree(&hsmooth) ;
}
printf("%d of %d (%2.1f%%) samples deleted\n",
num, nsamples, 100.0f*(float)num/(float)nsamples) ;
return(NO_ERROR) ;
}
MRI *
MRIsynthesizeWeightedVolume(MRI *mri_T1, MRI *mri_PD, float w5, float TR5,
float w30, float TR30, float target_wm, float TE) {
MRI *mri_dst ;
int x, y, z, width, height, depth ;
MRI *mri30, *mri5 ;
Real val30, val5, val, min_val ;
#if 0
int mri_peak, n, min_real_bin ;
double mean_PD ;
MRI_REGION box ;
HISTOGRAM *h_mri, *h_smooth ;
float x0, y0, z0, min_real_val ;
#endif
width = mri_T1->width ;
height = mri_T1->height ;
depth = mri_T1->depth ;
mri_dst = MRIalloc(width, height, depth, MRI_FLOAT) ;
MRIcopyHeader(mri_T1, mri_dst) ;
mri30 = MRIsynthesize(mri_T1, mri_PD, NULL, NULL, TR30, RADIANS(30), TE) ;
mri5 = MRIsynthesize(mri_T1, mri_PD, NULL, NULL, TR5, RADIANS(5), TE) ;
#if 0
mean_PD = MRImeanFrame(mri_PD, 0) ;
/* MRIscalarMul(mri_PD, mri_PD, 1000.0f/mean_PD) ;*/
h_mri = MRIhistogram(mri30, 100) ;
h_smooth = HISTOsmooth(h_mri, NULL, 2) ;
mri_peak = HISTOfindHighestPeakInRegion(h_smooth, 0, h_smooth->nbins) ;
min_real_bin = HISTOfindNextValley(h_smooth, mri_peak) ;
min_real_val = h_smooth->bins[min_real_bin] ;
MRIfindApproximateSkullBoundingBox(mri30, min_real_val, &box) ;
x0 = box.x+box.dx/3 ;
y0 = box.y+box.dy/3 ;
z0 = box.z+box.dz/2 ;
printf("using (%.0f, %.0f, %.0f) as brain centroid...\n",x0, y0, z0) ;
box.dx /= 4 ;
box.x = x0 - box.dx/2;
box.dy /= 4 ;
box.y = y0 - box.dy/2;
box.dz /= 4 ;
box.z = z0 - box.dz/2;
printf("using box (%d,%d,%d) --> (%d, %d,%d) "
"to find MRI wm\n", box.x, box.y, box.z,
box.x+box.dx-1,box.y+box.dy-1, box.z+box.dz-1) ;
h_mri = MRIhistogramRegion(mri30, 0, NULL, &box) ;
for (n = 0 ; n < h_mri->nbins-1 ; n++)
if (h_mri->bins[n+1] > min_real_val)
break ;
HISTOclearBins(h_mri, h_mri, 0, n) ;
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON)
HISTOplot(h_mri, "mri.histo") ;
mri_peak = HISTOfindLastPeak(h_mri, HISTO_WINDOW_SIZE,MIN_HISTO_PCT);
mri_peak = h_mri->bins[mri_peak] ;
printf("before smoothing, mri peak at %d\n", mri_peak) ;
h_smooth = HISTOsmooth(h_mri, NULL, 2) ;
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON)
HISTOplot(h_smooth, "mri_smooth.histo") ;
mri_peak = HISTOfindLastPeak(h_smooth, HISTO_WINDOW_SIZE,MIN_HISTO_PCT);
mri_peak = h_mri->bins[mri_peak] ;
printf("after smoothing, mri peak at %d\n", mri_peak) ;
HISTOfree(&h_smooth) ;
HISTOfree(&h_mri) ;
#endif
min_val = 0 ;
for (x = 0 ; x < width ; x++) {
for (y = 0 ; y < height ; y++) {
for (z = 0 ; z < depth ; z++) {
if (x == Gx && y == Gy && z == Gz)
DiagBreak() ;
MRIsampleVolumeType(mri30, x, y, z, &val30, SAMPLE_NEAREST) ;
MRIsampleVolumeType(mri5, x, y, z, &val5, SAMPLE_NEAREST) ;
val = w30*val30 + w5*val5 ;
MRIFvox(mri_dst, x, y, z) = val ;
if (val < min_val)
min_val = val ;
}
}
}
for (x = 0 ; x < width ; x++) {
for (y = 0 ; y < height ; y++) {
for (z = 0 ; z < depth ; z++) {
if (x == Gx && y == Gy && z == Gz)
DiagBreak() ;
MRIFvox(mri_dst, x, y, z) += min_val ;
}
}
}
MRIfree(&mri30) ;
MRIfree(&mri5) ;
return(mri_dst) ;
}
