NAME

       Atropos - part of ANTS registration suite

DESCRIPTION

   COMMAND:
              ./Atropos

              A  finite  mixture  modeling  (FMM)  segmentation approach with possibilities for specifying prior
              constraints. These prior constraints include the specification  of  a  prior  label  image,  prior
              probability  images  (one for each class), and/or an MRF prior to enforce spatial smoothing of the
              labels. Similar algorithms include FAST and SPM.

   OPTIONS:

       -d, --image-dimensionality 2/3/4

              This option forces the image to be treated as a specified-dimensional  image.  If  not  specified,
              Atropos tries to infer the dimensionality from the first input image.

       -a, --intensity-image [intensityImage,<adaptiveSmoothingWeight>]

              One or more scalar images is specified for segmentation using the -a/--intensity-image option. For
              segmentation  scenarios  with  no  prior  information,  the  first scalar image encountered on the
              command line is used to order labelings such that the class with the smallest intensity  signature
              is  class '1' through class 'N' which represents the voxels with the largest intensity values. The
              optional adaptive smoothing weight  parameter  is  applicable  only  when  using  prior  label  or
              probability  images.  This  scalar  parameter  is to be specified between [0,1] which smooths each
              labeled region separately and modulates the intensity measurement at each voxel in each  intensity
              image  between  the original intensity and its smoothed counterpart. The smoothness parameters are
              governed by the -b/--bspline option.

       -b, --bspline [<numberOfLevels=6>,<initialMeshResolution=1x1x...>,<splineOrder=3>]

              If the adaptive smoothing weights are > 0, the intensity images are smoothed  in  calculating  the
              likelihood  values.  This  is  to  account for subtle intensity differences across the same tissue
              regions.

       -i, --initialization Random[numberOfClasses]
              Otsu[numberOfClasses] KMeans[numberOfClasses,<clusterCenters(in  ascending  order  and  for  first
              intensity   image   only)>]   PriorProbabilityImages[numberOfClasses,fileSeriesFormat(index=1   to
              numberOfClasses)            or             vectorImage,priorWeighting,<priorProbabilityThreshold>]
              PriorLabelImage[numberOfClasses,labelImage,priorWeighting]

              To  initialize the FMM parameters, one of the following options must be specified. If one does not
              have prior label or probability images we recommend using kmeans as it is  typically  faster  than
              otsu  and can be used with multivariate initialization. However, since a Euclidean distance on the
              inter cluster distances is used, one might  have  to  appropriately  scale  the  additional  input
              images.  Random  initialization  is  meant purely for intellectual curiosity.  The prior weighting
              (specified in the range [0,1]) is used to modulate the calculation of the posterior  probabilities
              between  the  likelihood*mrfprior  and  the  likelihood*mrfprior*prior.  For specifying many prior
              probability images for a multi-label segmentation, we offer a minimize usage option (see -m). With
              that option one can specify a prior probability threshold in which  only  those  pixels  exceeding
              that threshold are stored in memory.

       -p, --posterior-formulation Socrates[<useMixtureModelProportions=1>]
              Plato[<useMixtureModelProportions=1>] Aristotle[<useMixtureModelProportions=1>]

              Different  posterior  probability formulations are possible which include the following: Socrates:
              posteriorProbability  =  (spatialPrior)^priorWeight*(likelihood*mrfPrior)^(1-priorWeight),  Plato:
              posteriorProbability = 1.0, Aristotle: posteriorProbability = 1.0,

       -x, --mask-image maskImageFilename

              The  image  mask  (which  is  required)  defines  the region which is to be labeled by the Atropos
              algorithm.

       -c, --convergence [<numberOfIterations=5>,<convergenceThreshold=0.001>]

              Convergence is determined by calculating the mean maximum posterior probability over the region of
              interest at each iteration. When this  value  decreases  or  increases  less  than  the  specified
              threshold  from the previous iteration or the maximum number of iterations is exceeded the program
              terminates.

       -k, --likelihood-model Gaussian
              HistogramParzenWindows[<sigma=1.0>,<numberOfBins=32>]
              ManifoldParzenWindows[<pointSetSigma=1.0>,<evaluationKNeighborhood=50>,<CovarianceKNeighborhood=0>,<kernelSigma=0>]
              LogEuclideanGaussian

              Both parametric and non-parametric options exist in Atropos. The  Gaussian  parametric  option  is
              commonly  used  (e.g.  SPM  & FAST) where the mean and standard deviation for the Gaussian of each
              class is calculated at each iteration. Other groups use non-parametric approaches  exemplified  by
              option 2. We recommend using options 1 or 2 as they are fairly standard and the default parameters
              work adequately.

       -m, --mrf [<smoothingFactor=0.3>,<radius=1x1x...>]

              Markov  random  field (MRF) theory provides a general framework for enforcing spatially contextual
              constraints on the segmentation solution. The default smoothing factor of 0.3 provides a  moderate
              amount  of  smoothing.  Increasing this number causes more smoothing whereas decreasing the number
              lessens the smoothing. The radius parameter specifies the mrf neighborhood.

       -o, --output [classifiedImage,<posteriorProbabilityImageFileNameFormat>]

              The output consists of a labeled image where each voxel in the masked region is assigned  a  label
              from  1,  2, ..., N. Optionally, one can also output the posterior probability images specified in
              the same format as the prior probability images,  e.g.  posterior%02d.nii.gz  (C-style  file  name
              formatting).

       -u, --minimize-memory-usage (0)/1

              By default, memory usage is not minimized, however, if this is needed, the various probability and
              distance  images  are  calculated  on the fly instead of being stored in memory at each iteration.
              Also, if prior probability images are used, only the non-negligible pixel  values  are  stored  in
              memory.  <VALUES>: 0

       -w, --winsorize-outliers BoxPlot[<lowerPercentile=0.25>,<upperPercentile=0.75>,<whiskerLength=1.5>]
              GrubbsRosner[<significanceLevel=0.05>,<winsorizingLevel=0.10>]

              To  remove  the  effects of outliers in calculating the weighted mean and weighted covariance, the
              user can opt to remove the outliers through the options specified below.

       -e, --use-euclidean-distance (0)/1

              Given prior label or probability images, the labels are propagated throughout the masked region so
              that every voxel in the mask is labeled. Propagation is done by using a signed distance  transform
              of  the label. Alternatively, propagation of the labels with the fast marching filter respects the
              distance along the shape of the mask (e.g. the sinuous sulci and gyri of the cortex.  <VALUES>: 0

       -l, --label-propagation whichLabel[lambda=0.0,<boundaryProbability=1.0>]

              The propagation of each prior label can be controlled  by  the  lambda  and  boundary  probability
              parameters.  The  latter  parameter  is  the  probability (in the range [0,1]) of the label on the
              boundary which increases linearly to a maximum value of 1.0 in the interior of the labeled region.
              The former parameter dictates the exponential decay of probability propagation outside the labeled
              region from the boundary probability, i.e. boundaryProbability*exp( -lambda * distance ).

       -h

              Print the help menu (short version).  <VALUES>: 0

       --help

              Print the help menu.  <VALUES>: 1, 0

Atropos 1.9                                         May 2012                                          ATROPOS(1)