jointfusion (1)

Joint Label Fusion: usage:

• jointfusion dim mod [options] output_image

required options:

dim

Image dimension (2 or 3)

mod

Number of modalities or features

-g atlas1_mod1.nii atlas1_mod2.nii ...atlasN_mod1.nii atlasN_mod2.nii ...

Warped atlas images

-tg target_mod1.nii ... target_modN.nii

Target image(s)

-l label1.nii ... labelN.nii

Warped atlas segmentation

-m <method> [parameters]

Select voting method. Options: Joint (Joint Label Fusion) May be followed by optional parameters in brackets, e.g., -m Joint[0.1,2]. See below for parameters

other options:

-rp radius

Patch radius for similarity measures, scalar or vector (AxBxC) Default: 2x2x2

-rs radius

Local search radius. Default: 3x3x3

-x label image.nii

Specify an exclusion region for the given label.

-p filenamePattern

Save the posterior maps (probability that each voxel belongs to each label) as images. The number of images saved equals the number of labels. The filename pattern must be in C printf format, e.g. posterior%04d.nii.gz

-w filenamePattern

Save the voting weights as images. The number of images saved equals the number of atlases. The filename pattern must be in C printf format, e.g. weight%04d.nii.gz

-gp ID_atlas1 ... ID_atlasN

Assign a group ID for each atlas

-gpw weight_gp1 ... weight_gpn

Assign the voting weights to each atlas group

Parameters for -m Joint option:

alpha

Regularization term added to matrix Mx for inverse Default: 0.1

beta

Exponent for mapping intensity difference to joint error Default: 2