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) ; }