static float
compute_overlap(MRI *mri_seg1, MRI *mri_seg2, TRANSFORM *transform1,
TRANSFORM *transform2) {
int x, y, z, width, height, depth, l1, l2, x2, y2, z2 ;
float overlap, x1, y1, z1 ;
TransformInvert(transform1, mri_seg1) ;
width = mri_seg1->width ;
height = mri_seg1->height ;
depth = mri_seg1->depth ;
for (overlap = 0.0f, x = 0 ; x < width ; x++) {
for (y = 0 ; y < height ; y++) {
for (z = 0 ; z < depth ; z++) {
l1 = MRIvox(mri_seg1, x, y, z) ;
TransformSample(transform1, x, y, z, &x1, &y1, &z1) ; /* atlas coords of s1 */
TransformSampleInverseVoxel(transform2, width, height, depth,
nint(x1), nint(y1), nint(z1), &x2, &y2, &z2) ;
l2 = MRIvox(mri_seg2, x2, y2, z2) ;
if (l1 == l2)
overlap++ ;
}
}
}
return(overlap) ;
}
static int
get_option(int argc, char *argv[])
{
int nargs = 0 ;
char *option ;
option = argv[1] + 1 ; /* past '-' */
StrUpper(option) ;
if (!stricmp(option, "debug_voxel"))
{
Gsx = Gx = atoi(argv[2]) ;
Gsy = Gy = atoi(argv[3]) ;
Gsz = Gz = atoi(argv[4]) ;
nargs = 3 ;
printf("debugging voxel (%d, %d, %d)\n", Gx, Gy, Gz) ;
}
else if (!stricmp(option, "debug_node"))
{
Gx = atoi(argv[2]) ;
Gy = atoi(argv[3]) ;
Gz = atoi(argv[4]) ;
nargs = 3 ;
printf("debugging node (%d, %d, %d)\n", Gx, Gy, Gz) ;
}
else if (!stricmp(option, "OPTIMAL"))
{
mp.integration_type = GCAM_INTEGRATE_OPTIMAL ;
printf("using optimal time-step integration\n") ;
}
else if (!stricmp(option, "NONEG")) {
mp.noneg = atoi(argv[2]) ;
nargs = 1 ;
printf("%s allowing temporary folds during numerical minimization\n",
mp.noneg ? "not" : "") ;
}
else if (!stricmp(option, "renormalize")) {
renormalize = atoi(argv[2]) ;
nargs = 1 ;
printf("%srenormalizing intensities\n", renormalize ? "" : "not ");
if (renormalize == 0)
{
match_mean_intensity = match_peak_intensity_ratio = 0 ;
}
}
else if (!stricmp(option, "aseg")) {
match_mean_intensity = match_peak_intensity_ratio = 0 ;
use_aseg = 1 ;
mp.l_dtrans = 1 ;
mp.l_log_likelihood = 0 ;
renormalize = 0 ;
printf("treating inputs as segmentations\n") ;
mp.dtrans_labels = dtrans_labels ;
mp.ndtrans = NDTRANS_LABELS ;
}
else if (!stricmp(option, "diag2")) {
mp.mri_diag2 = MRIread(argv[2]) ;
if (mp.mri_diag2 == NULL)
ErrorExit(ERROR_NOFILE, "%s: could not read diag volume from %s", Progname, argv[2]) ;
nargs = 1 ;
printf("writing d2 diagnostics for input volume %s\n", argv[2]) ;
}
else if (!stricmp(option, "MOMENTUM") || !stricmp(option, "FIXED"))
{
mp.integration_type = GCAM_INTEGRATE_FIXED ;
printf("using optimal time-step integration\n") ;
}
else if (!stricmp(option, "distance"))
{
distance = atof(argv[2]) ;
nargs = 1 ;
printf("expanding border by %2.1f mm every outer cycle\n", distance);
}
else if (!stricmp(option, "dtrans"))
{
mp.l_dtrans = atof(argv[2]) ;
nargs = 1 ;
printf("setting distance transform coefficient to %2.3f\n", mp.l_dtrans) ;
}
else if (!stricmp(option, "match_peak"))
{
match_peak_intensity_ratio = 1 ;
match_mean_intensity = 0 ;
printf("matching peak of intensity ratio histogram\n") ;
}
else if (!stricmp(option, "erode"))
{
erosions = atoi(argv[2]) ;
nargs = 1 ;
printf("eroding source and target image %d times before morphing\n",
erosions) ;
}
else if (!stricmp(option, "match_mean"))
{
match_peak_intensity_ratio = 0 ;
match_mean_intensity = atoi(argv[2]) ;
nargs = 1 ;
printf("%smatching peak of intensity ratio histogram\n",
match_mean_intensity ? "" : "not ") ;
}
else if (!stricmp(option, "intensity") ||!stricmp(option, "ll"))
{
mp.l_log_likelihood = atof(argv[2]) ;
nargs = 1 ;
printf("setting l_log_likelihood = %2.3f\n", mp.l_log_likelihood );
}
else if (!stricmp(option, "noregrid"))
{
regrid = 0 ;
mp.regrid = False ;
printf("disabling regridding...\n") ;
}
else if (!stricmp(option, "regrid"))
{
regrid = 1 ;
mp.regrid = True ;
printf("enabling regridding...\n") ;
}
else if (!stricmp(option, "view"))
{
Gsx = atoi(argv[2]) ;
Gsy = atoi(argv[3]) ;
Gsz = atoi(argv[4]) ;
nargs = 3 ;
printf("viewing voxel (%d, %d, %d)\n", Gsx, Gsy, Gsz) ;
}
else if (!stricmp(option, "LEVELS"))
{
mp.levels = atoi(argv[2]) ;
nargs = 1 ;
printf("levels = %d\n", mp.levels) ;
}
else if (!stricmp(option, "area_smoothness") || !stricmp(option, "asmooth"))
{
mp.l_area_smoothness = atof(argv[2]) ;
nargs = 1 ;
printf("using l_area_smoothness=%2.3f\n", mp.l_area_smoothness) ;
}
else if (!stricmp(option, "area"))
{
mp.l_area = atof(argv[2]) ;
nargs = 1 ;
printf("using l_area=%2.3f\n", mp.l_area) ;
}
else if (!stricmp(option, "area_intensity"))
{
mp.l_area_intensity = atof(argv[2]) ;
nargs = 1 ;
printf("using l_area_intensity=%2.3f\n", mp.l_area_intensity) ;
}
else if (!stricmp(option, "tol"))
{
mp.tol = atof(argv[2]) ;
nargs = 1 ;
printf("using tol=%2.3f\n", mp.tol) ;
}
else if (!stricmp(option, "sigma"))
{
mp.sigma = atof(argv[2]) ;
nargs = 1 ;
printf("using sigma=%2.3f\n", mp.sigma) ;
}
else if (!stricmp(option, "min_sigma"))
{
mp.min_sigma = atof(argv[2]) ;
nargs = 1 ;
printf("using min sigma=%2.3f\n", mp.min_sigma) ;
}
else if (!stricmp(option, "ribbon"))
{
ribbon_name = argv[2] ;
printf("reading ribbon from %s and inserting into aseg\n", ribbon_name) ;
nargs = 1 ;
}
else if (!stricmp(option, "rthresh"))
{
mp.ratio_thresh = atof(argv[2]) ;
mp.uncompress = 1 ;
nargs = 1 ;
printf("using compression ratio threshold = %2.3f...\n", mp.ratio_thresh) ;
}
else if (!stricmp(option, "scale"))
{
scale_values = atof(argv[2]) ;
nargs = 1 ;
printf("scaling input values by %2.3f\n", scale_values) ;
}
else if (!stricmp(option, "dt"))
{
mp.dt = atof(argv[2]) ;
nargs = 1 ;
printf("using dt = %2.3f\n", mp.dt) ;
}
else if (!stricmp(option, "passes"))
{
mp.npasses = atoi(argv[2]) ;
nargs = 1 ;
printf("integrating in %d passes (default=3)\n", mp.npasses) ;
}
else if (!stricmp(option, "skip"))
{
skip = atoi(argv[2]);
printf("skipping %d voxels in source data...\n", skip) ;
nargs = 1 ;
}
else if (!stricmp(option, "apply"))
{
apply_transform = atoi(argv[2]) ;
nargs = 1 ;
printf("%sapplying transform after registration\n", apply_transform ? "" : "not ") ;
}
else switch (*option)
{
case 'D':
mp.l_distance = atof(argv[2]) ;
nargs = 1 ;
printf("using l_distance = %2.3f\n", mp.l_distance) ;
break ;
case 'M':
mp.momentum = atof(argv[2]) ;
nargs = 1 ;
printf("momentum = %2.2f\n", mp.momentum) ;
break ;
case 'N':
mp.niterations = atoi(argv[2]) ;
nargs = 1 ;
printf("using niterations = %d\n", mp.niterations) ;
break ;
case 'S':
mp.l_smoothness = atof(argv[2]) ;
nargs = 1 ;
printf("using l_smoothness = %2.3f\n", mp.l_smoothness) ;
break ;
case 'T':
printf("reading transform from %s...\n", argv[2]) ;
transform = TransformRead(argv[2]) ;
if (transform == NULL)
ErrorExit(ERROR_NOFILE,"%s: could not read transform from %s\n",Progname,argv[2]);
nargs = 1 ;
if (transform->type == LINEAR_VOX_TO_VOX)
{
printf("converting transform to ras....\n") ;
LTAvoxelToRasXform((LTA *)(transform->xform), NULL, NULL) ;
}
break ;
case 'I':
printf("reading transform from %s...\n", argv[2]) ;
transform = TransformRead(argv[2]) ;
if (transform == NULL)
ErrorExit(ERROR_NOFILE,"%s: could not read transform from %s\n",Progname,argv[2]);
TransformInvert(transform, NULL) ;
TransformSwapInverse(transform) ;
if (transform->type == LINEAR_VOX_TO_VOX)
{
printf("converting transform to ras....\n") ;
LTAvoxelToRasXform((LTA *)(transform->xform), NULL, NULL) ;
}
nargs = 1 ;
break ;
case 'B':
mp.l_binary = atof(argv[2]) ;
nargs = 1 ;
printf("using l_binary=%2.3f\n", mp.l_binary) ;
break ;
case 'J':
mp.l_jacobian = atof(argv[2]) ;
nargs = 1 ;
printf("using l_jacobian=%2.3f\n", mp.l_jacobian) ;
break ;
case 'Z':
nozero = (atoi(argv[2]) == 0) ;
printf("%sdisabling zero image locations\n", nozero ? "" : "not ") ;
nargs = 1 ;
break ;
case 'A':
mp.navgs = atoi(argv[2]) ;
nargs = 1 ;
printf("smoothing gradient with %d averages...\n", mp.navgs) ;
break ;
case 'K':
mp.exp_k = atof(argv[2]) ;
printf("setting exp_k to %2.2f (default=%2.2f)\n",
mp.exp_k, EXP_K) ;
nargs = 1 ;
break ;
case 'W':
mp.write_iterations = atoi(argv[2]) ;
Gdiag |= DIAG_WRITE ;
nargs = 1 ;
printf("setting write iterations = %d\n", mp.write_iterations) ;
break ;
case '?':
case 'U':
usage_exit(1);
break ;
default:
printf("unknown option %s\n", argv[1]) ;
usage_exit(1) ;
break ;
}
return(nargs) ;
}
int
main(int argc, char *argv[])
{
char **av ;
int ac, nargs, n ;
MRI *mri_src, *mri_dst = NULL, *mri_bias, *mri_orig, *mri_aseg = NULL ;
char *in_fname, *out_fname ;
int msec, minutes, seconds ;
struct timeb start ;
char cmdline[CMD_LINE_LEN] ;
make_cmd_version_string
(argc, argv,
"$Id: mri_normalize.c,v 1.80 2012/10/16 21:38:35 nicks Exp $",
"$Name: $",
cmdline);
/* rkt: check for and handle version tag */
nargs = handle_version_option
(argc, argv,
"$Id: mri_normalize.c,v 1.80 2012/10/16 21:38:35 nicks Exp $",
"$Name: $");
if (nargs && argc - nargs == 1)
{
exit (0);
}
argc -= nargs;
Progname = argv[0] ;
ErrorInit(NULL, NULL, NULL) ;
DiagInit(NULL, NULL, NULL) ;
mni.max_gradient = MAX_GRADIENT ;
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) ;
}
if (argc < 1)
{
ErrorExit(ERROR_BADPARM, "%s: no input name specified", Progname) ;
}
in_fname = argv[1] ;
if (argc < 2)
{
ErrorExit(ERROR_BADPARM, "%s: no output name specified", Progname) ;
}
out_fname = argv[2] ;
if(verbose)
{
printf( "reading from %s...\n", in_fname) ;
}
mri_src = MRIread(in_fname) ;
if (!mri_src)
ErrorExit(ERROR_NO_FILE, "%s: could not open source file %s",
Progname, in_fname) ;
MRIaddCommandLine(mri_src, cmdline) ;
if(nsurfs > 0)
{
MRI_SURFACE *mris ;
MRI *mri_dist=NULL, *mri_dist_sup=NULL, *mri_ctrl, *mri_dist_one ;
LTA *lta= NULL ;
int i ;
TRANSFORM *surface_xform ;
if (control_point_fname) // do one pass with only file control points first
{
MRI3dUseFileControlPoints(mri_src, control_point_fname) ;
mri_dst =
MRI3dGentleNormalize(mri_src,
NULL,
DEFAULT_DESIRED_WHITE_MATTER_VALUE,
NULL,
intensity_above,
intensity_below/2,1,
bias_sigma, mri_not_control);
}
else
{
mri_dst = MRIcopy(mri_src, NULL) ;
}
for (i = 0 ; i < nsurfs ; i++)
{
mris = MRISread(surface_fnames[i]) ;
if (mris == NULL)
ErrorExit(ERROR_NOFILE,"%s: could not surface %s",
Progname,surface_fnames[i]);
surface_xform = surface_xforms[i] ;
TransformInvert(surface_xform, NULL) ;
if (surface_xform->type == MNI_TRANSFORM_TYPE ||
surface_xform->type == TRANSFORM_ARRAY_TYPE ||
surface_xform->type == REGISTER_DAT)
{
lta = (LTA *)(surface_xform->xform) ;
#if 0
if (invert)
{
VOL_GEOM vgtmp;
LT *lt;
MATRIX *m_tmp = lta->xforms[0].m_L ;
lta->xforms[0].m_L = MatrixInverse(lta->xforms[0].m_L, NULL) ;
MatrixFree(&m_tmp) ;
lt = <a->xforms[0];
if (lt->dst.valid == 0 || lt->src.valid == 0)
{
printf( "WARNING:***************************************************************\n");
printf( "WARNING:dst volume infor is invalid. Most likely produce wrong inverse.\n");
printf( "WARNING:***************************************************************\n");
}
copyVolGeom(<->dst, &vgtmp);
copyVolGeom(<->src, <->dst);
copyVolGeom(&vgtmp, <->src);
}
#endif
}
if (stricmp(surface_xform_fnames[i], "identity.nofile") != 0)
{
MRIStransform(mris, NULL, surface_xform, NULL) ;
}
mri_dist_one = MRIcloneDifferentType(mri_dst, MRI_FLOAT) ;
printf("computing distance transform\n") ;
MRIScomputeDistanceToSurface(mris, mri_dist_one, mri_dist_one->xsize) ;
if (i == 0)
{
mri_dist = MRIcopy(mri_dist_one, NULL) ;
}
else
{
MRIcombineDistanceTransforms(mri_dist_one, mri_dist, mri_dist) ;
}
// MRIminAbs(mri_dist_one, mri_dist, mri_dist) ;
MRIfree(&mri_dist_one) ;
}
MRIscalarMul(mri_dist, mri_dist, -1) ;
if (nonmax_suppress)
{
printf("computing nonmaximum suppression\n") ;
mri_dist_sup = MRInonMaxSuppress(mri_dist, NULL, 0, 1) ;
mri_ctrl = MRIcloneDifferentType(mri_dist_sup, MRI_UCHAR) ;
MRIbinarize(mri_dist_sup, mri_ctrl, min_dist, CONTROL_NONE, CONTROL_MARKED) ;
}
else if (erode)
{
int i ;
mri_ctrl = MRIcloneDifferentType(mri_dist, MRI_UCHAR) ;
MRIbinarize(mri_dist, mri_ctrl, min_dist, CONTROL_NONE, CONTROL_MARKED) ;
for (i = 0 ; i < erode ; i++)
{
MRIerode(mri_ctrl, mri_ctrl) ;
}
}
else
{
mri_ctrl = MRIcloneDifferentType(mri_dist, MRI_UCHAR) ;
MRIbinarize(mri_dist, mri_ctrl, min_dist, CONTROL_NONE, CONTROL_MARKED) ;
}
if (control_point_fname)
{
MRInormAddFileControlPoints(mri_ctrl, CONTROL_MARKED) ;
}
if (mask_sigma > 0)
{
MRI *mri_smooth, *mri_mag, *mri_grad ;
mri_smooth = MRIgaussianSmooth(mri_dst, mask_sigma, 1, NULL) ;
mri_mag = MRIcloneDifferentType(mri_dst, MRI_FLOAT) ;
mri_grad = MRIsobel(mri_smooth, NULL, mri_mag) ;
MRIbinarize(mri_mag, mri_mag, mask_thresh, 1, 0) ;
MRImask(mri_ctrl, mri_mag, mri_ctrl, 0, CONTROL_NONE) ;
MRIfree(&mri_grad) ;
MRIfree(&mri_mag) ;
MRIfree(&mri_smooth) ;
}
if (mask_orig_fname)
{
MRI *mri_orig ;
mri_orig = MRIread(mask_orig_fname) ;
MRIbinarize(mri_orig, mri_orig, mask_orig_thresh, 0, 1) ;
MRImask(mri_ctrl, mri_orig, mri_ctrl, 0, CONTROL_NONE) ;
MRIfree(&mri_orig) ;
}
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON)
{
MRIwrite(mri_dist, "d.mgz");
MRIwrite(mri_dist_sup, "dm.mgz");
MRIwrite(mri_ctrl, "c.mgz");
}
MRIeraseBorderPlanes(mri_ctrl, 4) ;
if (aseg_fname)
{
mri_aseg = MRIread(aseg_fname) ;
if (mri_aseg == NULL)
{
ErrorExit(ERROR_NOFILE,
"%s: could not load aseg from %s", Progname, aseg_fname) ;
}
remove_nonwm_voxels(mri_ctrl, mri_aseg, mri_ctrl) ;
MRIfree(&mri_aseg) ;
}
else
{
remove_surface_outliers(mri_ctrl, mri_dist, mri_dst, mri_ctrl) ;
}
mri_bias = MRIbuildBiasImage(mri_dst, mri_ctrl, NULL, 0.0) ;
if (mri_dist)
{
MRIfree(&mri_dist) ;
}
if (mri_dist_sup)
{
MRIfree(&mri_dist_sup) ;
}
if (bias_sigma> 0)
{
MRI *mri_kernel = MRIgaussian1d(bias_sigma, -1) ;
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON)
{
MRIwrite(mri_bias, "b.mgz") ;
}
printf("smoothing bias field\n") ;
MRIconvolveGaussian(mri_bias, mri_bias, mri_kernel) ;
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON)
{
MRIwrite(mri_bias, "bs.mgz") ;
}
MRIfree(&mri_kernel);
}
MRIfree(&mri_ctrl) ;
mri_dst = MRIapplyBiasCorrectionSameGeometry
(mri_dst, mri_bias, mri_dst,
DEFAULT_DESIRED_WHITE_MATTER_VALUE) ;
printf("writing normalized volume to %s\n", out_fname) ;
MRIwrite(mri_dst, out_fname) ;
exit(0) ;
} // end if(surface_fname)
if (!mriConformed(mri_src) && conform > 0)
{
printf("unconformed source detected - conforming...\n") ;
mri_src = MRIconform(mri_src) ;
}
if (mask_fname)
{
MRI *mri_mask ;
mri_mask = MRIread(mask_fname) ;
if (!mri_mask)
ErrorExit(ERROR_NOFILE, "%s: could not open mask volume %s.\n",
Progname, mask_fname) ;
MRImask(mri_src, mri_mask, mri_src, 0, 0) ;
MRIfree(&mri_mask) ;
}
if (read_flag)
{
MRI *mri_ctrl ;
double scale ;
mri_bias = MRIread(bias_volume_fname) ;
if (!mri_bias)
ErrorExit
(ERROR_BADPARM,
"%s: could not read bias volume %s", Progname, bias_volume_fname) ;
mri_ctrl = MRIread(control_volume_fname) ;
if (!mri_ctrl)
ErrorExit
(ERROR_BADPARM,
"%s: could not read control volume %s",
Progname, control_volume_fname) ;
MRIbinarize(mri_ctrl, mri_ctrl, 1, 0, 128) ;
mri_dst = MRImultiply(mri_bias, mri_src, NULL) ;
scale = MRImeanInLabel(mri_dst, mri_ctrl, 128) ;
printf("mean in wm is %2.0f, scaling by %2.2f\n", scale, 110/scale) ;
scale = 110/scale ;
MRIscalarMul(mri_dst, mri_dst, scale) ;
MRIwrite(mri_dst, out_fname) ;
exit(0) ;
}
if(long_flag)
{
MRI *mri_ctrl ;
double scale ;
mri_bias = MRIread(long_bias_volume_fname) ;
if (!mri_bias)
ErrorExit
(ERROR_BADPARM,
"%s: could not read bias volume %s", Progname, long_bias_volume_fname) ;
mri_ctrl = MRIread(long_control_volume_fname) ;
if (!mri_ctrl)
ErrorExit
(ERROR_BADPARM,
"%s: could not read control volume %s",
Progname, long_control_volume_fname) ;
MRIbinarize(mri_ctrl, mri_ctrl, 1, 0, CONTROL_MARKED) ;
if (mri_ctrl->type != MRI_UCHAR)
{
MRI *mri_tmp ;
mri_tmp = MRIchangeType(mri_ctrl, MRI_UCHAR, 0, 1,1);
MRIfree(&mri_ctrl) ;
mri_ctrl = mri_tmp ;
}
scale = MRImeanInLabel(mri_src, mri_ctrl, CONTROL_MARKED) ;
printf("mean in wm is %2.0f, scaling by %2.2f\n", scale, 110/scale) ;
scale = DEFAULT_DESIRED_WHITE_MATTER_VALUE/scale ;
mri_dst = MRIscalarMul(mri_src, NULL, scale) ;
MRIremoveWMOutliers(mri_dst, mri_ctrl, mri_ctrl, intensity_below/2) ;
mri_bias = MRIbuildBiasImage(mri_dst, mri_ctrl, NULL, 0.0) ;
MRIsoapBubble(mri_bias, mri_ctrl, mri_bias, 50, 1) ;
MRIapplyBiasCorrectionSameGeometry(mri_dst, mri_bias, mri_dst,
DEFAULT_DESIRED_WHITE_MATTER_VALUE);
// MRIwrite(mri_dst, out_fname) ;
// exit(0) ;
} // end if(long_flag)
if (grad_thresh > 0)
{
float thresh ;
MRI *mri_mag, *mri_grad, *mri_smooth ;
MRI *mri_kernel = MRIgaussian1d(.5, -1) ;
mri_not_control = MRIcloneDifferentType(mri_src, MRI_UCHAR) ;
switch (scan_type)
{
case MRI_MGH_MPRAGE:
thresh = 15 ;
break ;
case MRI_WASHU_MPRAGE:
thresh = 20 ;
break ;
case MRI_UNKNOWN:
default:
thresh = 12 ;
break ;
}
mri_smooth = MRIconvolveGaussian(mri_src, NULL, mri_kernel) ;
thresh = grad_thresh ;
mri_mag = MRIcloneDifferentType(mri_src, MRI_FLOAT) ;
mri_grad = MRIsobel(mri_smooth, NULL, mri_mag) ;
MRIwrite(mri_mag, "m.mgz") ;
MRIbinarize(mri_mag, mri_not_control, thresh, 0, 1) ;
MRIwrite(mri_not_control, "nc.mgz") ;
MRIfree(&mri_mag) ;
MRIfree(&mri_grad) ;
MRIfree(&mri_smooth) ;
MRIfree(&mri_kernel) ;
}
#if 0
#if 0
if ((mri_src->type != MRI_UCHAR) ||
(!(mri_src->xsize == 1 && mri_src->ysize == 1 && mri_src->zsize == 1)))
#else
if (conform || (mri_src->type != MRI_UCHAR && conform > 0))
#endif
{
MRI *mri_tmp ;
fprintf
(stderr,
"downsampling to 8 bits and scaling to isotropic voxels...\n") ;
mri_tmp = MRIconform(mri_src) ;
mri_src = mri_tmp ;
}
#endif
if(aseg_fname)
{
printf("Reading aseg %s\n",aseg_fname);
mri_aseg = MRIread(aseg_fname) ;
if (mri_aseg == NULL)
ErrorExit
(ERROR_NOFILE,
"%s: could not read aseg from file %s", Progname, aseg_fname) ;
if (!mriConformed(mri_aseg))
{
ErrorExit(ERROR_UNSUPPORTED, "%s: aseg volume %s must be conformed",
Progname, aseg_fname) ;
}
}
else
{
mri_aseg = NULL ;
}
if(verbose)
{
printf( "normalizing image...\n") ;
}
fflush(stdout);
fflush(stderr);
TimerStart(&start) ;
if (control_point_fname)
{
MRI3dUseFileControlPoints(mri_src, control_point_fname) ;
}
// this just setup writing control-point volume saving
if(control_volume_fname)
{
MRI3dWriteControlPoints(control_volume_fname) ;
}
/* first do a gentle normalization to get
things in the right intensity range */
if(long_flag == 0) // if long, then this will already have been done with base control points
{
if(control_point_fname != NULL) /* do one pass with only
file control points first */
mri_dst =
MRI3dGentleNormalize(mri_src,
NULL,
DEFAULT_DESIRED_WHITE_MATTER_VALUE,
NULL,
intensity_above,
intensity_below/2,1,
bias_sigma, mri_not_control);
else
{
mri_dst = MRIcopy(mri_src, NULL) ;
}
}
fflush(stdout);
fflush(stderr);
if(mri_aseg)
{
MRI *mri_ctrl, *mri_bias ;
int i ;
printf("processing with aseg\n");
mri_ctrl = MRIclone(mri_aseg, NULL) ;
for (i = 0 ; i < NWM_LABELS ; i++)
{
MRIcopyLabel(mri_aseg, mri_ctrl, aseg_wm_labels[i]) ;
}
printf("removing outliers in the aseg WM...\n") ;
MRIremoveWMOutliersAndRetainMedialSurface(mri_dst,
mri_ctrl,
mri_ctrl,
intensity_below) ;
MRIbinarize(mri_ctrl, mri_ctrl, 1, CONTROL_NONE, CONTROL_MARKED) ;
MRInormAddFileControlPoints(mri_ctrl, CONTROL_MARKED) ;
if (interior_fname1)
{
MRIS *mris_interior1, *mris_interior2 ;
mris_interior1 = MRISread(interior_fname1) ;
if (mris_interior1 == NULL)
ErrorExit(ERROR_NOFILE,
"%s: could not read white matter surface from %s\n",
Progname, interior_fname1) ;
mris_interior2 = MRISread(interior_fname2) ;
if (mris_interior2 == NULL)
ErrorExit(ERROR_NOFILE,
"%s: could not read white matter surface from %s\n",
Progname, interior_fname2) ;
add_interior_points(mri_ctrl,
mri_dst,
intensity_above,
1.25*intensity_below,
mris_interior1,
mris_interior2,
mri_aseg,
mri_ctrl) ;
MRISfree(&mris_interior1) ;
MRISfree(&mris_interior2) ;
}
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON)
{
MRIwrite(mri_ctrl, "norm_ctrl.mgz") ;
}
printf("Building bias image\n");
fflush(stdout);
fflush(stderr);
mri_bias = MRIbuildBiasImage(mri_dst, mri_ctrl, NULL, 0.0) ;
fflush(stdout);
fflush(stderr);
if (bias_sigma> 0)
{
printf("Smoothing with sigma %g\n",bias_sigma);
MRI *mri_kernel = MRIgaussian1d(bias_sigma, -1) ;
MRIconvolveGaussian(mri_bias, mri_bias, mri_kernel) ;
MRIfree(&mri_kernel);
fflush(stdout);
fflush(stderr);
}
MRIfree(&mri_ctrl) ;
MRIfree(&mri_aseg) ;
printf("Applying bias correction\n");
mri_dst = MRIapplyBiasCorrectionSameGeometry
(mri_dst, mri_bias, mri_dst,
DEFAULT_DESIRED_WHITE_MATTER_VALUE) ;
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON)
{
MRIwrite(mri_dst, "norm_1.mgz") ;
}
fflush(stdout);
fflush(stderr);
} // if(mri_aseg)
else
{
printf("processing without aseg, no1d=%d\n",no1d);
if (!no1d)
{
printf("MRInormInit(): \n");
MRInormInit(mri_src, &mni, 0, 0, 0, 0, 0.0f) ;
printf("MRInormalize(): \n");
mri_dst = MRInormalize(mri_src, NULL, &mni) ;
if (!mri_dst)
{
no1d = 1 ;
printf("1d normalization failed - trying no1d...\n") ;
// ErrorExit(ERROR_BADPARM, "%s: normalization failed", Progname) ;
}
}
if(no1d)
{
if ((file_only && nosnr) ||
((gentle_flag != 0) && (control_point_fname != NULL)))
{
if (mri_dst == NULL)
{
mri_dst = MRIcopy(mri_src, NULL) ;
}
}
else
{
if (nosnr)
{
if (interior_fname1)
{
MRIS *mris_interior1, *mris_interior2 ;
MRI *mri_ctrl ;
printf("computing initial normalization using surface interiors\n");
mri_ctrl = MRIcloneDifferentType(mri_src, MRI_UCHAR) ;
mris_interior1 = MRISread(interior_fname1) ;
if (mris_interior1 == NULL)
ErrorExit(ERROR_NOFILE,
"%s: could not read white matter surface from %s\n",
Progname, interior_fname1) ;
mris_interior2 = MRISread(interior_fname2) ;
if (mris_interior2 == NULL)
ErrorExit(ERROR_NOFILE,
"%s: could not read white matter surface from %s\n",
Progname, interior_fname2) ;
add_interior_points(mri_ctrl,
mri_dst,
intensity_above,
1.25*intensity_below,
mris_interior1,
mris_interior2,
mri_aseg,
mri_ctrl) ;
MRISfree(&mris_interior1) ;
MRISfree(&mris_interior2) ;
mri_bias = MRIbuildBiasImage(mri_dst, mri_ctrl, NULL, 0.0) ;
if (bias_sigma> 0)
{
MRI *mri_kernel = MRIgaussian1d(bias_sigma, -1) ;
MRIconvolveGaussian(mri_bias, mri_bias, mri_kernel) ;
MRIfree(&mri_kernel);
}
mri_dst = MRIapplyBiasCorrectionSameGeometry
(mri_src,
mri_bias,
mri_dst,
DEFAULT_DESIRED_WHITE_MATTER_VALUE) ;
MRIfree(&mri_ctrl) ;
}
else if (long_flag == 0) // no initial normalization specified
{
mri_dst = MRIcopy(mri_src, NULL) ;
}
}
else
{
printf("computing initial normalization using SNR...\n") ;
mri_dst = MRInormalizeHighSignalLowStd
(mri_src, mri_dst, bias_sigma,
DEFAULT_DESIRED_WHITE_MATTER_VALUE) ;
}
}
if (!mri_dst)
ErrorExit
(ERROR_BADPARM, "%s: could not allocate volume", Progname) ;
}
} // else (not using aseg)
fflush(stdout);
fflush(stderr);
if (file_only == 0)
MRI3dGentleNormalize(mri_dst, NULL, DEFAULT_DESIRED_WHITE_MATTER_VALUE,
mri_dst,
intensity_above, intensity_below/2,
file_only, bias_sigma, mri_not_control);
mri_orig = MRIcopy(mri_dst, NULL) ;
printf("\n");
printf("Iterating %d times\n",num_3d_iter);
for (n = 0 ; n < num_3d_iter ; n++)
{
if(file_only)
{
break ;
}
printf( "---------------------------------\n");
printf( "3d normalization pass %d of %d\n", n+1, num_3d_iter) ;
if (gentle_flag)
MRI3dGentleNormalize(mri_dst, NULL, DEFAULT_DESIRED_WHITE_MATTER_VALUE,
mri_dst,
intensity_above/2, intensity_below/2,
file_only, bias_sigma, mri_not_control);
else
MRI3dNormalize(mri_orig, mri_dst, DEFAULT_DESIRED_WHITE_MATTER_VALUE,
mri_dst,
intensity_above, intensity_below,
file_only, prune, bias_sigma, scan_type, mri_not_control);
}
printf( "Done iterating ---------------------------------\n");
// this just setup writing control-point volume saving
if(control_volume_fname)
{
MRI3dWriteControlPoints(control_volume_fname) ;
}
if(bias_volume_fname)
{
mri_bias = compute_bias(mri_src, mri_dst, NULL) ;
printf("writing bias field to %s....\n", bias_volume_fname) ;
MRIwrite(mri_bias, bias_volume_fname) ;
MRIfree(&mri_bias) ;
}
if (verbose)
{
printf("writing output to %s\n", out_fname) ;
}
MRIwrite(mri_dst, out_fname) ;
msec = TimerStop(&start) ;
MRIfree(&mri_src);
MRIfree(&mri_dst);
seconds = nint((float)msec/1000.0f) ;
minutes = seconds / 60 ;
seconds = seconds % 60 ;
printf( "3D bias adjustment took %d minutes and %d seconds.\n",
minutes, seconds) ;
exit(0) ;
return(0) ;
}
int
main(int argc, char *argv[]) {
char **av, fname[STRLEN], *out_fname, *subject_name, *cp ;
int ac, nargs, i, n, noint = 0, options ;
int msec, minutes, seconds, nsubjects, input ;
struct timeb start ;
GCA *gca ;
MRI *mri_seg, *mri_tmp, *mri_inputs ;
TRANSFORM *transform ;
LTA *lta;
GCA_BOUNDARY *gcab ;
Progname = argv[0] ;
ErrorInit(NULL, NULL, NULL) ;
DiagInit(NULL, NULL, NULL) ;
TimerStart(&start) ;
parms.use_gradient = 0 ;
spacing = 8 ;
/* rkt: check for and handle version tag */
nargs = handle_version_option
(argc, argv,
"$Id: mri_gcab_train.c,v 1.4 2011/03/16 20:23:33 fischl Exp $",
"$Name: $");
if (nargs && argc - nargs == 1)
exit (0);
argc -= nargs;
// parse command line args
ac = argc ;
av = argv ;
for ( ; argc > 1 && ISOPTION(*argv[1]) ; argc--, argv++) {
nargs = get_option(argc, argv) ;
argc -= nargs ;
argv += nargs ;
}
printf("reading gca from %s\n", argv[1]) ;
gca = GCAread(argv[1]) ;
if (!gca)
exit(Gerror) ;
if (!strlen(subjects_dir)) /* hasn't been set on command line */
{
cp = getenv("SUBJECTS_DIR") ;
if (!cp)
ErrorExit(ERROR_BADPARM, "%s: SUBJECTS_DIR not defined in environment",
Progname);
strcpy(subjects_dir, cp) ;
if (argc < 4)
usage_exit(1) ;
}
// options parsed. subjects and gca name remaining
out_fname = argv[argc-1] ;
nsubjects = argc-3 ;
for (options = i = 0 ; i < nsubjects ; i++) {
if (argv[i+1][0] == '-') {
nsubjects-- ;
options++ ;
}
}
printf("training on %d subject and writing results to %s\n",
nsubjects, out_fname) ;
n = 0 ;
gcab = GCABalloc(gca, 8, 0, 30, 10, target_label);
strcpy(gcab->gca_fname, argv[1]) ;
// going through the subject one at a time
for (nargs = i = 0 ; i < nsubjects+options ; i++) {
subject_name = argv[i+2] ;
//////////////////////////////////////////////////////////////
printf("***************************************"
"************************************\n");
printf("processing subject %s, %d of %d...\n", subject_name,i+1-nargs,
nsubjects);
if (stricmp(subject_name, "-NOINT") == 0) {
printf("not using intensity information for subsequent subjects...\n");
noint = 1 ;
nargs++ ;
continue ;
} else if (stricmp(subject_name, "-INT") == 0) {
printf("using intensity information for subsequent subjects...\n");
noint = 0 ;
nargs++ ;
continue ;
}
// reading this subject segmentation
sprintf(fname, "%s/%s/mri/%s", subjects_dir, subject_name, seg_dir) ;
if (Gdiag & DIAG_SHOW && DIAG_VERBOSE_ON)
fprintf(stderr, "Reading segmentation from %s...\n", fname) ;
mri_seg = MRIread(fname) ;
if (!mri_seg)
ErrorExit(ERROR_NOFILE, "%s: could not read segmentation file %s",
Progname, fname) ;
if ((mri_seg->type != MRI_UCHAR) && (mri_seg->type != MRI_FLOAT)) {
ErrorExit
(ERROR_NOFILE,
"%s: segmentation file %s is not type UCHAR or FLOAT",
Progname, fname) ;
}
if (binarize) {
int j ;
for (j = 0 ; j < 256 ; j++) {
if (j == binarize_in)
MRIreplaceValues(mri_seg, mri_seg, j, binarize_out) ;
else
MRIreplaceValues(mri_seg, mri_seg, j, 0) ;
}
}
if (insert_fname) {
MRI *mri_insert ;
sprintf(fname, "%s/%s/mri/%s",
subjects_dir, subject_name, insert_fname) ;
mri_insert = MRIread(fname) ;
if (mri_insert == NULL)
ErrorExit(ERROR_NOFILE,
"%s: could not read volume from %s for insertion",
Progname, insert_fname) ;
MRIbinarize(mri_insert, mri_insert, 1, 0, insert_label) ;
MRIcopyLabel(mri_insert, mri_seg, insert_label) ;
MRIfree(&mri_insert) ;
}
replaceLabels(mri_seg) ;
MRIeraseBorderPlanes(mri_seg, 1) ;
for (input = 0 ; input < gca->ninputs ; input++) {
//////////// set the gca type //////////////////////////////
// is this T1/PD training?
// how can we allow flash data training ???????
// currently checks the TE, TR, FA to be the same for all inputs
// thus we cannot allow flash data training.
////////////////////////////////////////////////////////////
sprintf(fname, "%s/%s/mri/%s",
subjects_dir, subject_name,input_names[input]);
if (DIAG_VERBOSE_ON)
printf("reading co-registered input from %s...\n", fname) ;
fprintf(stderr, " reading input %d: %s\n", input, fname);
mri_tmp = MRIread(fname) ;
if (!mri_tmp)
ErrorExit
(ERROR_NOFILE,
"%s: could not read image from file %s", Progname, fname) ;
// input check 1
if (getSliceDirection(mri_tmp) != MRI_CORONAL) {
ErrorExit
(ERROR_BADPARM,
"%s: must be in coronal direction, but it is not\n",
fname);
}
// input check 2
if (mri_tmp->xsize != 1 || mri_tmp->ysize != 1 || mri_tmp->zsize != 1) {
ErrorExit
(ERROR_BADPARM,
"%s: must have 1mm voxel size, but have (%f, %f, %f)\n",
fname, mri_tmp->xsize, mri_tmp->ysize, mri_tmp->ysize);
}
// input check 3 is removed. now we can handle c_(ras) != 0 case
// input check 4
if (i == 0) {
TRs[input] = mri_tmp->tr ;
FAs[input] = mri_tmp->flip_angle ;
TEs[input] = mri_tmp->te ;
} else if (!FEQUAL(TRs[input],mri_tmp->tr) ||
!FEQUAL(FAs[input],mri_tmp->flip_angle) ||
!FEQUAL(TEs[input], mri_tmp->te))
ErrorExit
(ERROR_BADPARM,
"%s: subject %s input volume %s: sequence parameters "
"(%2.1f, %2.1f, %2.1f)"
"don't match other inputs (%2.1f, %2.1f, %2.1f)",
Progname, subject_name, fname,
mri_tmp->tr, DEGREES(mri_tmp->flip_angle), mri_tmp->te,
TRs[input], DEGREES(FAs[input]), TEs[input]) ;
// first time do the following
if (input == 0) {
int nframes = gca->ninputs ;
///////////////////////////////////////////////////////////
mri_inputs =
MRIallocSequence(mri_tmp->width, mri_tmp->height, mri_tmp->depth,
mri_tmp->type, nframes) ;
if (!mri_inputs)
ErrorExit
(ERROR_NOMEMORY,
"%s: could not allocate input volume %dx%dx%dx%d",
mri_tmp->width, mri_tmp->height, mri_tmp->depth,nframes) ;
MRIcopyHeader(mri_tmp, mri_inputs) ;
}
// -mask option ////////////////////////////////////////////
if (mask_fname)
{
MRI *mri_mask ;
sprintf(fname, "%s/%s/mri/%s",
subjects_dir, subject_name, mask_fname);
printf("reading volume %s for masking...\n", fname) ;
mri_mask = MRIread(fname) ;
if (!mri_mask)
ErrorExit(ERROR_NOFILE, "%s: could not open mask volume %s.\n",
Progname, fname) ;
MRImask(mri_tmp, mri_mask, mri_tmp, 0, 0) ;
MRIfree(&mri_mask) ;
}
MRIcopyFrame(mri_tmp, mri_inputs, 0, input) ;
MRIfree(&mri_tmp) ;
}// end of inputs per subject
/////////////////////////////////////////////////////////
// xform_name is given, then we can use the consistent c_(r,a,s) for gca
/////////////////////////////////////////////////////////
if (xform_name)
{
// we read talairach.xfm which is a RAS-to-RAS
sprintf(fname, "%s/%s/mri/transforms/%s",
subjects_dir, subject_name, xform_name) ;
if (Gdiag & DIAG_SHOW && DIAG_VERBOSE_ON)
printf("INFO: reading transform file %s...\n", fname);
if (!FileExists(fname))
{
fprintf(stderr,"ERROR: cannot find transform file %s\n",fname);
exit(1);
}
transform = TransformRead(fname);
if (!transform)
ErrorExit(ERROR_NOFILE, "%s: could not read transform from file %s",
Progname, fname);
modify_transform(transform, mri_inputs, gca);
// Here we do 2 things
// 1. modify gca direction cosines to
// that of the transform destination (both linear and non-linear)
// 2. if ras-to-ras transform,
// then change it to vox-to-vox transform (linear case)
// modify transform to store inverse also
TransformInvert(transform, mri_inputs) ;
// verify inverse
lta = (LTA *) transform->xform;
}
else
{
GCAreinit(mri_inputs, gca);
// just use the input value, since dst = src volume
transform = TransformAlloc(LINEAR_VOXEL_TO_VOXEL, NULL) ;
}
////////////////////////////////////////////////////////////////////
// train gca
////////////////////////////////////////////////////////////////////
// segmentation is seg volume
// inputs is the volumes of all inputs
// transform is for this subject
// noint is whether to use intensity information or not
GCABtrain(gcab, mri_inputs, mri_seg, transform, target_label) ;
MRIfree(&mri_seg) ;
MRIfree(&mri_inputs) ;
TransformFree(&transform) ;
}
GCABcompleteTraining(gcab) ;
if (smooth > 0) {
printf("regularizing conditional densities with smooth=%2.2f\n", smooth) ;
GCAregularizeConditionalDensities(gca, smooth) ;
}
if (navgs) {
printf("applying mean filter %d times to conditional densities\n", navgs) ;
GCAmeanFilterConditionalDensities(gca, navgs) ;
}
printf("writing trained GCAB to %s...\n", out_fname) ;
if (GCABwrite(gcab, out_fname) != NO_ERROR)
ErrorExit
(ERROR_BADFILE, "%s: could not write gca to %s", Progname, out_fname) ;
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON)
{
MRI *mri ;
mri = GCAbuildMostLikelyVolume(gca, NULL) ;
MRIwrite(mri, "m.mgz") ;
MRIfree(&mri) ;
}
if (histo_fname) {
FILE *fp ;
int histo_counts[10000], xn, yn, zn, max_count ;
GCA_NODE *gcan ;
memset(histo_counts, 0, sizeof(histo_counts)) ;
fp = fopen(histo_fname, "w") ;
if (!fp)
ErrorExit(ERROR_BADFILE, "%s: could not open histo file %s",
Progname, histo_fname) ;
max_count = 0 ;
for (xn = 0 ; xn < gca->node_width; xn++) {
for (yn = 0 ; yn < gca->node_height ; yn++) {
for (zn = 0 ; zn < gca->node_depth ; zn++) {
gcan = &gca->nodes[xn][yn][zn] ;
if (gcan->nlabels < 1)
continue ;
if (gcan->nlabels == 1 && IS_UNKNOWN(gcan->labels[0]))
continue ;
histo_counts[gcan->nlabels]++ ;
if (gcan->nlabels > max_count)
max_count = gcan->nlabels ;
}
}
}
max_count = 20 ;
for (xn = 1 ; xn < max_count ; xn++)
fprintf(fp, "%d %d\n", xn, histo_counts[xn]) ;
fclose(fp) ;
}
GCAfree(&gca) ;
msec = TimerStop(&start) ;
seconds = nint((float)msec/1000.0f) ;
minutes = seconds / 60 ;
seconds = seconds % 60 ;
printf("classifier array training took %d minutes"
" and %d seconds.\n", minutes, seconds) ;
exit(0) ;
return(0) ;
}
int compute_tissue_modes( MRI *mri_inputs,
GCA *gca,
GCA_SAMPLE *gcas,
TRANSFORM *transform,
int nsamples,
double *pwm, double *pgm, double *pfluid )
{
int x, y, z, width, height, depth, i, xp, yp, zp ;
float vals[MAX_GCA_INPUTS] ;
int countOutside = 0, ngm, nwm, nfluid;
double gm, wm, fluid ;
/* go through each GC in the sample and compute the probability of
the image at that point.
*/
width = mri_inputs->width ;
height = mri_inputs->height;
depth = mri_inputs->depth ;
// store inverse transformation .. forward:input->gca template,
// inv: gca template->input
TransformInvert(transform, mri_inputs) ;
// go through all sample points
for (ngm = nwm = nfluid = 0, wm = gm = fluid = 0.0, i = 0 ;
i < nsamples ;
i++)
{
/////////////////// diag code /////////////////////////////
if (i == Gdiag_no)
{
DiagBreak() ;
}
if (Gdiag_no == gcas[i].label)
{
DiagBreak() ;
}
if (i == Gdiag_no ||
(gcas[i].xp == Gxp && gcas[i].yp == Gyp && gcas[i].zp == Gzp))
{
DiagBreak() ;
}
///////////////////////////////////////////////////////////
// get prior coordinates
xp = gcas[i].xp ;
yp = gcas[i].yp ;
zp = gcas[i].zp ;
// if it is inside the source voxel
if (!GCApriorToSourceVoxel(gca, mri_inputs, transform,
xp, yp, zp, &x, &y, &z))
{
if (x == Gx && y == Gy && z == Gz)
{
DiagBreak() ;
}
// (x,y,z) is the source voxel position
gcas[i].x = x ;
gcas[i].y = y ;
gcas[i].z = z ;
// get values from all inputs
load_vals(mri_inputs, x, y, z, vals, gca->ninputs) ;
if (FZERO(vals[0]) && gcas[i].label == Gdiag_no)
{
DiagBreak() ;
}
if (gcas[i].tissue_class == GM_CLASS)
{
ngm++ ;
gm += vals[0] ;
}
else if (gcas[i].tissue_class == WM_CLASS)
{
nwm++ ;
wm += vals[0] ;
}
else if (gcas[i].tissue_class == FLUID_CLASS)
{
nfluid++ ;
fluid += vals[0] ;
}
if (!FZERO(vals[0]))
{
DiagBreak() ;
}
if (gcas[i].label != Unknown)
{
DiagBreak() ;
}
if (i == Gdiag_no)
{
DiagBreak() ;
}
}
else // outside the volume
{
countOutside++;
}
}
if (nfluid == 0)
{
nfluid = 1 ;
}
if (ngm == 0)
{
ngm = 1 ;
}
if (nwm == 0)
{
nwm = 1 ;
}
wm /= nwm ;
gm /= ngm ;
fluid /= nfluid ;
G_wm_mean = *pwm = wm ;
G_gm_mean = *pgm = gm ;
G_fluid_mean = *pfluid = fluid ;
return(NO_ERROR) ;
}
int
main(int argc, char *argv[]) {
char *gca_fname, *in_fname, **av, *xform_fname ;
MRI *mri_in, *mri_tmp, *mri_orig = NULL ;
GCA *gca ;
int ac, nargs, input, ninputs ;
TRANSFORM *transform = NULL ;
char cmdline[CMD_LINE_LEN] ;
double ll ;
make_cmd_version_string
(argc, argv,
"$Id: mri_log_likelihood.c,v 1.4 2011/03/02 00:04:22 nicks Exp $",
"$Name: stable5 $", cmdline);
/* rkt: check for and handle version tag */
nargs = handle_version_option
(argc, argv,
"$Id: mri_log_likelihood.c,v 1.4 2011/03/02 00:04:22 nicks Exp $",
"$Name: stable5 $");
if (nargs && argc - nargs == 1)
exit (0);
argc -= nargs;
setRandomSeed(-1L) ;
Progname = argv[0] ;
DiagInit(NULL, NULL, NULL) ;
ErrorInit(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)
ErrorExit
(ERROR_BADPARM,
"usage: %s [<options>] <inbrain1> <inbrain2> ... "
"<atlas> <transform file> ...\n",
Progname) ;
ninputs = (argc - 1) / 2 ;
if (DIAG_VERBOSE_ON)
printf("reading %d input volume%ss\n", ninputs, ninputs > 1 ? "s" : "") ;
in_fname = argv[1] ;
gca_fname = argv[1+ninputs] ;
xform_fname = argv[2+ninputs] ;
transform = TransformRead(xform_fname) ;
if (!transform)
ErrorExit(ERROR_NOFILE, "%s: could not read input transform from %s",
Progname, xform_fname) ;
if (DIAG_VERBOSE_ON)
printf("reading atlas from '%s'...\n", gca_fname) ;
gca = GCAread(gca_fname) ;
if (!gca)
ErrorExit(ERROR_NOFILE, "%s: could not read input atlas from %s",
Progname, gca_fname) ;
fflush(stdout) ;
for (input = 0 ; input < ninputs ; input++) {
in_fname = argv[1+input] ;
if (DIAG_VERBOSE_ON)
printf("reading input volume from %s...\n", in_fname) ;
mri_tmp = MRIread(in_fname) ;
if (!mri_tmp)
ErrorExit(ERROR_NOFILE, "%s: could not read input MR volume from %s",
Progname, in_fname) ;
MRImakePositive(mri_tmp, mri_tmp) ;
if (input == 0) {
mri_in =
MRIallocSequence(mri_tmp->width, mri_tmp->height, mri_tmp->depth,
mri_tmp->type, ninputs) ;
if (!mri_in)
ErrorExit(ERROR_NOMEMORY,
"%s: could not allocate input volume %dx%dx%dx%d",
mri_tmp->width,mri_tmp->height,mri_tmp->depth,ninputs) ;
MRIcopyHeader(mri_tmp, mri_in) ;
}
MRIcopyFrame(mri_tmp, mri_in, 0, input) ;
MRIfree(&mri_tmp) ;
}
MRIaddCommandLine(mri_in, cmdline) ;
TransformInvert(transform, mri_in) ;
if (orig_fname) {
mri_orig = MRIread(orig_fname) ;
if (mri_orig == NULL)
ErrorExit(ERROR_NOFILE, "%s: could not read orig volume from %s", Progname, orig_fname) ;
}
ll = GCAimageLogLikelihood(gca, mri_in, transform, 1, mri_orig) ;
printf("%2.0f\n", 10000*ll) ;
MRIfree(&mri_in) ;
if (gca)
GCAfree(&gca) ;
if (mri_in)
MRIfree(&mri_in) ;
exit(0) ;
return(0) ;
}
int
main(int argc, char *argv[])
{
char **av, fname[STRLEN], *out_fname, *subject_name, *cp, *tp1_name, *tp2_name ;
char s1_name[STRLEN], s2_name[STRLEN], *sname ;
int ac, nargs, i, n, options, max_index ;
int msec, minutes, seconds, nsubjects, input ;
struct timeb start ;
MRI *mri_seg, *mri_tmp, *mri_in ;
TRANSFORM *transform ;
// int counts ;
int t;
RANDOM_FOREST *rf = NULL ;
GCA *gca = NULL ;
Progname = argv[0] ;
ErrorInit(NULL, NULL, NULL) ;
DiagInit(NULL, NULL, NULL) ;
TimerStart(&start) ;
parms.width = parms.height = parms.depth = DEFAULT_VOLUME_SIZE ;
parms.ntrees = 10 ;
parms.max_depth = 10 ;
parms.wsize = 1 ;
parms.training_size = 100 ;
parms.training_fraction = .5 ;
parms.feature_fraction = 1 ;
/* rkt: check for and handle version tag */
nargs = handle_version_option
(argc, argv,
"$Id: mri_rf_long_train.c,v 1.5 2012/06/15 12:22:28 fischl Exp $",
"$Name: $");
if (nargs && argc - nargs == 1)
exit (0);
argc -= nargs;
// parse command line args
ac = argc ;
av = argv ;
for ( ; argc > 1 && ISOPTION(*argv[1]) ; argc--, argv++)
{
nargs = get_option(argc, argv) ;
argc -= nargs ;
argv += nargs ;
}
if (!strlen(subjects_dir)) /* hasn't been set on command line */
{
cp = getenv("SUBJECTS_DIR") ;
if (!cp)
ErrorExit(ERROR_BADPARM, "%s: SUBJECTS_DIR not defined in environment",
Progname);
strcpy(subjects_dir, cp) ;
}
if (argc < 3)
usage_exit(1) ;
// options parsed. subjects, tp1 and tp2 and rf name remaining
out_fname = argv[argc-1] ;
nsubjects = (argc-2)/3 ;
for (options = i = 0 ; i < nsubjects ; i++)
{
if (argv[i+1][0] == '-')
{
nsubjects-- ;
options++ ;
}
}
printf("training on %d subject and writing results to %s\n",
nsubjects, out_fname) ;
// rf_inputs can be T1, PD, ...per subject
if (parms.nvols == 0)
parms.nvols = ninputs ;
/* gca reads same # of inputs as we read
from command line - not the case if we are mapping to flash */
n = 0 ;
//////////////////////////////////////////////////////////////////
// set up gca direction cosines, width, height, depth defaults
gca = GCAread(gca_name) ;
if (gca == NULL)
ErrorExit(ERROR_NOFILE, "%s: could not read GCA from %s", Progname, gca_name) ;
/////////////////////////////////////////////////////////////////////////
// weird way options and subject name are mixed here
/////////////////////////////////////////////////////////
// first calculate mean
////////////////////////////////////////////////////////
// going through the subject one at a time
max_index = nsubjects+options ;
nargs = 0 ;
mri_in = NULL ;
#ifdef HAVE_OPENMP
subject_name = NULL ; sname = NULL ; t = 0 ;
// counts = 0 ; would be private
input = 0 ;
transform = NULL ;
tp1_name = tp2_name = NULL ;
mri_tmp = mri_seg = NULL ;
#pragma omp parallel for firstprivate(tp1_name, tp2_name, mri_in,mri_tmp, input, xform_name, transform, subjects_dir, force_inputs, conform, Progname, mri_seg, subject_name, s1_name, s2_name, sname, t, fname) shared(mri_inputs, transforms, mri_segs,argv) schedule(static,1)
#endif
for (i = 0 ; i < max_index ; i++)
{
subject_name = argv[3*i+1] ;
tp1_name = argv[3*i+2] ;
tp2_name = argv[3*i+3] ;
sprintf(s1_name, "%s_%s.long.%s_base", subject_name, tp1_name, subject_name) ;
sprintf(s2_name, "%s_%s.long.%s_base", subject_name, tp2_name, subject_name) ;
//////////////////////////////////////////////////////////////
printf("***************************************"
"************************************\n");
printf("processing subject %s, %d of %d (%s and %s)...\n", subject_name,i+1-nargs,
nsubjects, s1_name,s2_name);
for (t = 0 ; t < 2 ; t++)
{
sname = t == 0 ? s1_name : s2_name;
// reading this subject segmentation
sprintf(fname, "%s/%s/mri/%s", subjects_dir, sname, seg_dir) ;
if (Gdiag & DIAG_SHOW && DIAG_VERBOSE_ON)
fprintf(stderr, "Reading segmentation from %s...\n", fname) ;
mri_seg = MRIread(fname) ;
if (!mri_seg)
ErrorExit(ERROR_NOFILE, "%s: could not read segmentation file %s",
Progname, fname) ;
if ((mri_seg->type != MRI_UCHAR) && (make_uchar != 0))
{
MRI *mri_tmp ;
mri_tmp = MRIchangeType(mri_seg, MRI_UCHAR, 0, 1,1);
MRIfree(&mri_seg) ;
mri_seg = mri_tmp ;
}
if (wmsa_fname)
{
MRI *mri_wmsa ;
sprintf(fname, "%s/%s/mri/%s", subjects_dir, sname, wmsa_fname) ;
printf("reading WMSA labels from %s...\n", fname) ;
mri_wmsa = MRIread(fname) ;
if (mri_wmsa == NULL)
ErrorExit(ERROR_NOFILE, "%s: could not read WMSA file %s", fname) ;
MRIbinarize(mri_wmsa, mri_wmsa, 1, 0, WM_hypointensities) ;
MRIcopyLabel(mri_wmsa, mri_seg, WM_hypointensities) ;
lateralize_hypointensities(mri_seg) ;
if (Gdiag & DIAG_WRITE && DIAG_VERBOSE_ON )
{
char s[STRLEN] ;
sprintf(s, "%s/%s/mri/seg_%s",
subjects_dir, subject_name, wmsa_fname) ;
MRIwrite(mri_seg, s) ;
}
}
if (binarize)
{
int j ;
for (j = 0 ; j < 256 ; j++)
{
if (j == binarize_in)
MRIreplaceValues(mri_seg, mri_seg, j, binarize_out) ;
else
MRIreplaceValues(mri_seg, mri_seg, j, 0) ;
}
}
if (insert_fname)
{
MRI *mri_insert ;
sprintf(fname, "%s/%s/mri/%s",
subjects_dir, subject_name, insert_fname) ;
mri_insert = MRIread(fname) ;
if (mri_insert == NULL)
ErrorExit(ERROR_NOFILE,
"%s: could not read volume from %s for insertion",
Progname, insert_fname) ;
MRIbinarize(mri_insert, mri_insert, 1, 0, insert_label) ;
MRIcopyLabel(mri_insert, mri_seg, insert_label) ;
MRIfree(&mri_insert) ;
}
replaceLabels(mri_seg) ;
MRIeraseBorderPlanes(mri_seg, 1) ;
////////////////////////////////////////////////////////////
if (DIAG_VERBOSE_ON)
fprintf(stderr,
"Gather all input volumes for the subject %s.\n",
subject_name);
// inputs must be coregistered
// note that inputs are T1, PD, ... per subject (same TE, TR, FA)
for (input = 0 ; input < ninputs ; input++)
{
//////////// set the gca type //////////////////////////////
// is this T1/PD training?
// how can we allow flash data training ???????
// currently checks the TE, TR, FA to be the same for all inputs
// thus we cannot allow flash data training.
////////////////////////////////////////////////////////////
sprintf(fname, "%s/%s/mri/%s", subjects_dir, sname,input_names[input]);
if (DIAG_VERBOSE_ON)
printf("reading co-registered input from %s...\n", fname) ;
fprintf(stderr, " reading input %d: %s\n", input, fname);
mri_tmp = MRIread(fname) ;
if (!mri_tmp)
ErrorExit
(ERROR_NOFILE,
"%s: could not read image from file %s", Progname, fname) ;
// input check 1
if (getSliceDirection(mri_tmp) != MRI_CORONAL)
{
ErrorExit
(ERROR_BADPARM,
"%s: must be in coronal direction, but it is not\n",
fname);
}
// input check 2
if (conform &&
(mri_tmp->xsize != 1 || mri_tmp->ysize != 1 || mri_tmp->zsize != 1))
{
ErrorExit
(ERROR_BADPARM,
"%s: must have 1mm voxel size, but have (%f, %f, %f)\n",
fname, mri_tmp->xsize, mri_tmp->ysize, mri_tmp->ysize);
}
// input check 3 is removed. now we can handle c_(ras) != 0 case
// input check 4
if (i == 0)
{
TRs[input] = mri_tmp->tr ;
FAs[input] = mri_tmp->flip_angle ;
TEs[input] = mri_tmp->te ;
}
else if ((force_inputs == 0) &&
(!FEQUAL(TRs[input],mri_tmp->tr) ||
!FEQUAL(FAs[input],mri_tmp->flip_angle) ||
!FEQUAL(TEs[input], mri_tmp->te)))
ErrorExit
(ERROR_BADPARM,
"%s: subject %s input volume %s: sequence parameters "
"(%2.1f, %2.1f, %2.1f)"
"don't match other inputs (%2.1f, %2.1f, %2.1f)",
Progname, subject_name, fname,
mri_tmp->tr, DEGREES(mri_tmp->flip_angle), mri_tmp->te,
TRs[input], DEGREES(FAs[input]), TEs[input]) ;
// first time do the following
if (input == 0)
{
int nframes = ninputs ;
///////////////////////////////////////////////////////////
mri_in = MRIallocSequence(mri_tmp->width, mri_tmp->height, mri_tmp->depth,
mri_tmp->type, nframes) ;
if (!mri_in)
ErrorExit
(ERROR_NOMEMORY,
"%s: could not allocate input volume %dx%dx%dx%d",
mri_tmp->width, mri_tmp->height, mri_tmp->depth,nframes) ;
MRIcopyHeader(mri_tmp, mri_in) ;
}
// -mask option ////////////////////////////////////////////
if (mask_fname)
{
MRI *mri_mask ;
sprintf(fname, "%s/%s/mri/%s",
subjects_dir, subject_name, mask_fname);
printf("reading volume %s for masking...\n", fname) ;
mri_mask = MRIread(fname) ;
if (!mri_mask)
ErrorExit(ERROR_NOFILE, "%s: could not open mask volume %s.\n",
Progname, fname) ;
MRImask(mri_tmp, mri_mask, mri_tmp, 0, 0) ;
MRIfree(&mri_mask) ;
}
MRIcopyFrame(mri_tmp, mri_in, 0, input) ;
MRIfree(&mri_tmp) ;
}// end of inputs per subject
/////////////////////////////////////////////////////////
// xform_name is given, then we can use the consistent c_(r,a,s) for gca
/////////////////////////////////////////////////////////
if (xform_name)
{
// we read talairach.xfm which is a RAS-to-RAS
sprintf(fname, "%s/%s/mri/transforms/%s", subjects_dir, sname, xform_name) ;
if (Gdiag & DIAG_SHOW && DIAG_VERBOSE_ON)
printf("INFO: reading transform file %s...\n", fname);
if (!FileExists(fname))
{
fprintf(stderr,"ERROR: cannot find transform file %s\n",fname);
exit(1);
}
transform = TransformRead(fname);
if (!transform)
ErrorExit(ERROR_NOFILE, "%s: could not read transform from file %s",
Progname, fname);
// modify_transform(transform, mri_in, gca);
// Here we do 2 things
// 1. modify gca direction cosines to
// that of the transform destination (both linear and non-linear)
// 2. if ras-to-ras transform,
// then change it to vox-to-vox transform (linear case)
// modify transform to store inverse also
TransformInvert(transform, mri_in) ;
}
else
{
// GCAreinit(mri_in, gca);
// just use the input value, since dst = src volume
transform = TransformAlloc(LINEAR_VOXEL_TO_VOXEL, NULL) ;
}
/////////////////////////////////////////////////////////
if (do_sanity_check)
{
// conduct a sanity check of particular labels, most importantly
// hippocampus, that such labels do not exist in talairach coords
// where they are known not to belong (indicating a bad manual edit)
int errs = check(mri_seg, subjects_dir, subject_name);
if (errs)
{
printf(
"ERROR: mri_ca_train: possible bad training data! subject:\n"
"\t%s/%s\n\n", subjects_dir, subject_name);
fflush(stdout) ;
sanity_check_badsubj_count++;
}
}
mri_segs[i][t] = mri_seg ;
mri_inputs[i][t] = mri_in ;
transforms[i][t] = transform ;
}
}
rf = train_rforest(mri_inputs, mri_segs, transforms, nsubjects, gca, &parms, wm_thresh,wmsa_whalf, 2) ;
printf("writing random forest to %s\n", out_fname) ;
if (RFwrite(rf, out_fname) != NO_ERROR)
ErrorExit
(ERROR_BADFILE, "%s: could not write rf to %s", Progname, out_fname) ;
msec = TimerStop(&start) ;
seconds = nint((float)msec/1000.0f) ;
minutes = seconds / 60 ;
seconds = seconds % 60 ;
printf("classifier array training took %d minutes and %d seconds.\n", minutes, seconds) ;
exit(0) ;
return(0) ;
}
