3d+t Clustering segmentation, command-line version.
    Based on The C clustering library.
    Copyright (C) 2002 Michiel Jan Laurens de Hoon.
USAGE: 3dkmeans [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.
        -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
                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.