3d+t Clustering segmentation, command-line version.
Based on The C clustering library.
Copyright (C) 2002 Michiel Jan Laurens de Hoon.
USAGE: 3dkmeans [options]
options:
-v, --version Version information
-f filename: Input data to be clustered.
You can specify multiple filenames in sequence
and they will be catenated internally.
e.g: -f F1+orig F2+orig F3+orig ...
or -f F1+orig -f F2+orig -f F3+orig ...
-input filename: Same as -f
-mask mset Means to use the dataset 'mset' as a mask:
Only voxels with nonzero values in 'mset'
will be printed from 'dataset'. Note
that the mask dataset and the input dataset
must have the same number of voxels.
-mrange a b Means to further restrict the voxels from
'mset' so that only those mask values
between 'a' and 'b' (inclusive) will
be used. If this option is not given,
all nonzero values from 'mset' are used.
Note that if a voxel is zero in 'mset', then
it won't be included, even if a < 0 < b.
-cmask 'opts' Means to execute the options enclosed in single
quotes as a 3dcalc-like program, and produce
produce a mask from the resulting 3D brick.
Examples:
-cmask '-a fred+orig[7] -b zork+orig[3] -expr step(a-b)'
produces a mask that is nonzero only where
the 7th sub-brick of fred+orig is larger than
the 3rd sub-brick of zork+orig.
-cmask '-a fred+orig -expr 1-bool(k-7)'
produces a mask that is nonzero only in the
7th slice (k=7); combined with -mask, you
could use this to extract just selected voxels
from particular slice(s).
Notes: * You can use both -mask and -cmask in the same
run - in this case, only voxels present in
both masks will be dumped.
* Only single sub-brick calculations can be
used in the 3dcalc-like calculations -
if you input a multi-brick dataset here,
without using a sub-brick index, then only
its 0th sub-brick will be used.
* Do not use quotes inside the 'opts' string!
-u jobname Allows you to specify a different name for the
output files.
(default is derived from the input file name)
-prefix PREFIX Allows you to specify a prefix for the output
volumes. Default is the same as jobname
There are two output volumes, one for the cluster
membership and one with distance measures.
The distance dataset, mostly for debugging purposes
is formatted as follows:
Sub-brick 0: Dc = 100*(1-Ci)+100*Di/(Dmax)
with Ci the cluster number for voxel i, Di the
distance of voxel i to the centroid of its
assigned cluster, Dmax is the maximum distance in
cluster Ci.
Sub-bricks 1..k: Dc0k contains the distance of a
voxel's data to the centroid of cluster k.
Sub-brick k+1: Dc_norm = (1.0-Di/Ei)*100.0, where
Ei is the smallest distance of voxel i to
the remaining clusters that is larger than Di.
-g [0..8] Specifies distance measure for clustering
Note: Weight is a vector as long as the signatures
and used when computing distances. However for the
moment, all weights are set to 1
0: No clustering
1: Uncentered correlation distance
Same as Pearson distance, except
the means of v and s are not removed
when computing correlation.
2: Pearson distance
= (1-Weighted_Pearson_Correlation(v,s))
3: Uncentered correlation distance, absolute value
Same as abs(Pearson distance), except
the means of v and s are not removed
when computing correlation.
4: Pearson distance, absolute value
= (1-abs(Weighted_Pearson_Correlation(v,s)))
5: Spearman's rank distance
= (1-Spearman_Rank_Correlation(v,s))
No weighting is used
6: Kendall's distance
= (1-Kendall_Tau(v,s))
No weighting is used
7: Euclidean distance between v and s
= 1/sum(weight) * sum(weight[i]*(v[i]-s[i])^2)
8: City-block distance
= 1/sum(weight) * sum(weight[i]*abs(v[i]-s[i]))
(default for -g is 1, 7 if input has one value per voxel)
-k number Specify number of clusters
-remap METH Reassign clusters numbers based on METH:
NONE: No remapping (default)
COUNT: based on cluster size ascending
iCOUNT: COUNT, descending
MAG: based on ascending magnitude of centroid
iMAG: MAG, descending
-labeltable LTFILE: Attach labeltable LTFILE to clustering
output. This labeltable will overwrite
a table that is taken from CLUST_INIT
should you use -clust_init option.
-clabels LAB1 LAB2 ...: Provide a label for each cluster.
Labels cannot start with '-'.
-clust_init CLUST_INIT: Specify a dataset to initialize
clustering. This option sets -r 0 .
If CLUST_INIT has a labeltable and
you do not specify one then CLUST_INIT's
table is used for the output
-r number For k-means clustering, the number of times the
k-means clustering algorithm is run
(default: 0 with -clust_init, 1 otherwise)
-rsigs SIGS Calculate distances from each voxel's signature
to the signatures in SIGS.
SIGS is a multi-column 1D file with each column
being a signature.
The output is a dset the same size as the input
with as many sub-bricks as there are columns in
SIGS.
With this option, no clustering is done.
-verb verbose
-write_dists Output text files containing various measures.
FILE.kgg.1D : Cluster assignments
FILE.dis.1D : Distance between clusters
FILE.cen.1D : Cluster centroids
FILE.info1.1D: Within cluster sum of distances
FILE.info2.1D: Maximum distance within each cluster
FILE.vcd.1D: Distance from voxel to its centroid
-voxdbg I J K Output debugging info for voxel I J K
-seed SEED Seed for the random number generator.
