CIVET
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nuc_t1_native
skull_masking_native
stx_register
stx_tal_to_7
stx_tal_to_6
tal_t1
nuc_inorm_t1
skull_removal
nlfit
mask_classify
pve_curvature
pve
reclassify
segment
cls_volumes
cortical_masking
segment_volumes
surface_classify
artefact
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56.
create_wm_hemispheres
segment_mask
extract_white_surface_left
extract_white_surface_right
slide_left_hemi_obj_back
flip_right_hemi_obj_back
slide_right_hemi_obj_back
calibrate_left_white
calibrate_right_white
laplace_field
gray_surface_left
gray_surface_right
mid_surface_left
mid_surface_right
surface_fit_error
verify_image_nlfit
gyrification_index_left
verify_brain_mask
classify_qc
dataterm_left_surface
brain_mask_qc
gyrification_index_right
dataterm_right_surface
surface_registration_left
surface_registration_right
mean_curvature_20mm_left
mean_curvature_20mm_right
thickness_tlink_20mm_right
thickness_tlink_20mm_left
resample_left_mean_curvature
resample_right_mean_curvature
resample_right_thickness
resample_left_thickness
lobe_area_right
lobe_area_left
verify_clasp
verify_image
9. nlfit
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Label: "creation of nonlinear transform"
실행명령:
 best1stepnlreg.pl(s)
 mincresample (?)
 inormalize (?)
 minccalc (?)
 mincblur (?)
Input : civet_NL_19070095061210_t1_final.mnc
Output : civet_NL_19070095061210_t1_nlfit_It.xfm
best1stepnlreg.pl does hierachial non-linear fitting between two files
you will have to edit the script itself to modify the fitting levels
themselves
Input & output
(source.mnc)
(target.mnc)
10.Mask_classify
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Label: “tissue classification”
실행명령:
 classify_clean
 transform_tags
 Cleantag
 classify
Input : civet_NL_19070095061210_t1_final.mnc
civet_NL_19070095061210_skull_mask.mnc
Output : civet_NL_19070095061210_clean.mnc
의존성: nlfit
classify_clean is used to classify stereotaxic MINC volumes. It uses the classify
program (with mind-dist) and a set of standard sample points to compute an initial
volume classification. This classification is then optionally used to purge incorrect
tag points from the standard set, thus yielding a custom set of labels for the
particular subject. The tag point set is then used by an ANN classifier to classify the
volume.
10.Mask_classify
classify_clean [options] <in.mnc> [<in.mnc> ...] <classified.mnc>
classify_clean -clobber -clean_tags -mask_classified -mask_tag
-mask civet_NL_19070095061210_skull_mask.mnc
-tag_transform civet_NL_19070095061210_nlfit_It.xfm
civet_NL_19070095061210_t1_final.mnc
civet_NL_19070095061210_cls_clean.mnc
<option>
–
–
–
–
-clean_tags
clean tag file using mindist pre-classification [default is -noclean_tags]
-mask <mask.mnc> specify a mask volume
-maskbinvalue value of mask foreground [default: assume a binary mask]
-mask_tag
apply mask to foreground tags prior to classification(s) [default: -
–
-tag_transform non-linear transformation to map the tags from stereotaxic space to
subject
nomask_tag]
Input & output
civet_NL_19070095061210_cls_clean.mnc
Sub-routine
• transform_tags
– Transforms the input tags by the input transform. If a fourth argument is present, then
the inverse of the transform is used. The transformed tags are written to output.tag if
specified, otherwise input.tag is overwritten.
Usage : transform_tags input.tag input.xfm [output.tag] [invert]
transform_tags [invert]
ntags_1000_prob_90_nobg.tag
civet_NL_19070095061210_nlfit_It.xfm
nltransf_tag.tag (TEMP)
transform_tags [invert]
ntags_1000_bg.tag
civet_NL_19070095061210_nlfit_It.xfm
nltransf_bgtag.tag (TEMP)
Input & output
Input & output
Sub-routine
• Cleantags
Usage: cleantag [options] <fuzzy_class.mnc> <class_id> [<fuzzy_class.mnc> <class_id> ...]
cleantag
-oldtag nltransf_tag.tag
-newtag masked_standard.tag (TEMP)
-mask civet_NL_19070095061210_skull_mask.mnc
-maskbinvalue 1
< Option >
– oldtag : Specify the tag file to be cleaned
– newtag : Specify the file name of the clean set of tag points
– mask : Specify a mask to apply to the tag points
– maskbinvalue : Value of mask foreground Default value: 1
Input & output
Sub-routine
• Classify
Usage: classify <options> <infile1> [infile2] ... <outfile>
classify -verbose -clobber
-mask civet_NL_19070095061210_skull_mask.mnc
-user_mask_value 0.5
-min
-tagfile masked_standard.tag
-fuzzy all -fpath ./tmp/ -fprefix civet_NL_19070095061210_t1_final_fuzzy
civet_NL_19070095061210_t1_final.mnc
civet_NL_19070095061210_t1_final_fuzzy.mnc (TEMP)
< Option >
–
–
–
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Min : Use the 'Minimum Distance' classifier.
user_mask_value: Specify the mask value. (If the mask is a classified volume)
tagfile: Input training points as tag file (.tag)
fuzzy: Specify a string ex. '011...' to indicate classes for fuzzy classification.
Input & output
Sub-routine
• Cleantags
Usage: cleantag [options] <fuzzy_class.mnc> <class_id> [<fuzzy_class.mnc> <class_id> ...]
cleantag
-oldtag masked_standard.tag
-newtag civet_NL_19070095061210_t1_final_fuzzy_cleaned.tag (TEMP)
-mode 110 -threshold 0.7 -difference 0.3
-comment '-mode 110 -threshod 0.7 -difference 0.3‘
civet_NL_19070095061210_t1_final_fuzzy_1.mnc 1
civet_NL_19070095061210_t1_final_fuzzy_2.mnc 2
civet_NL_19070095061210_t1_final_fuzzy_3.mnc 3
< Option >
–
oldtag : Specify the tag file to be cleaned
–
newtag : Specify the file name of the clean set of tag points
–
Mode : Specify a quoted string 'xyz' to denote tag rejection mode.
Each of x,y,z being 0 or 1, effects are additive.
x = 1, if tag class label <> voxel class (highest fuzzy voxel class),
y = 1, if fuzzy voxel class < threshold,
z = 1, if diff. between 2 highest fuzzy voxel classes < diff. threshold,
–
threshold: Set the fuzzy threshold rejection criterion (applies to 'y' under –mode)
difference: Set the difference threshold rejection criterion (applies to 'z' under –mode)
Input & output
Sub-routine
• Cleantags
Usage: cleantag [options] <fuzzy_class.mnc> <class_id> [<fuzzy_class.mnc> <class_id> ...]
cleantag
-oldtag civet_NL_19070095061210_t1_final_fuzzy_cleaned.tag
-newtag masked_custom.tag
-mask civet_NL_19070095061210_skull_mask.mnc
-maskbinvalue 1
< Option >
– oldtag : Specify the tag file to be cleaned
– newtag : Specify the file name of the clean set of tag points
– mask : Specify a mask to apply to the tag points
– maskbinvalue : Value of mask foreground Default value: 1
Input & output
Sub-routine
• Classify
Usage: classify <options> <infile1> [infile2] ... <outfile>
classify -verbose -clobber
-mask civet_NL_19070095061210_skull_mask.mnc
-user_mask_value 0.5
-ann
-tagfile masked_custom (civet_NL_19070095061210_t1_final_fuzzy_custom.tag)
civet_NL_19070095061210_t1_final.mnc
civet_NL_19070095061210_cls_clean.mnc
< Option >
–
–
–
ann : Use the 'Artificial Neural Network' classifier.
user_mask_value: Specify the mask value. (If the mask is a classified volume)
tagfile: Input training points as tag file (.tag)
Input & output