itk_patch_morphology

Patch-based segmentation using expert priors (Coupe et al. 2010).

itk_patch_morphology <input> [output_labels] [options]

DESCRIPTION

itk_patch_morphology performs patch-based label fusion for anatomical segmentation using a library of pre-segmented template images. The method is based on the non-local means framework described by Coupe et al. (2010), where each voxel in the input image is labelled by comparing its local patch to patches from the training library and performing a weighted vote.

The similarity between patches is measured using a sum of squared differences, and the contribution of each training patch is weighted by an exponential function of the distance. This approach can achieve highly accurate segmentation when a sufficiently large and representative training library is available.

OPTIONS

--train data

Training library specification file containing image paths and labels.

--verbose

Print progress information during processing.

--quiet

Suppress all output messages.

--clobber

Overwrite the output file if it already exists.

--mask file

Restrict processing to voxels within the specified mask.

--patch n

Patch radius in voxels (default: 1).

--search n

Search radius in voxels (default: 1).

--cls output

Write the discrete classification output to the specified file.

--beta f

Smoothing parameter for exponential weighting (default: 0.125).

--scaling f

Apply intensity scaling factor to patches before comparison.

--threshold d

Threshold for minimum patch similarity.

--discrete n

Number of discrete classes (default: 2).

--confidence

Output confidence (weight) maps.

--adist

Output average distance maps.

--grading

Output grading score maps.

--iter n

Number of iterations for iterative refinement (default: 50).

--extract n

Extract features from the specified label.

--top n

Use only the top n most similar training patches.

--float

Store output voxels as single-precision floating point.

--short

Store output voxels as short integer.

--byte

Store output voxels as unsigned byte.

--box

Use a box-shaped (cubic) search neighbourhood.

--ball

Use a ball-shaped (spherical) search neighbourhood.

--prelabel

Use pre-existing labels to guide the segmentation.

--prob prefix

Write per-class probability maps with the given filename prefix.

--groups n

Number of training groups.

EXAMPLES

# Patch-based segmentation with a training library
itk_patch_morphology input.mnc output_labels.mnc --train library.csv \
    --mask brain_mask.mnc

# Segmentation with larger patch and search radius
itk_patch_morphology input.mnc output_labels.mnc --train library.csv \
    --mask brain_mask.mnc --patch 2 --search 2

# Output probability maps for each class
itk_patch_morphology input.mnc output_labels.mnc --train library.csv \
    --prob prob_ --float

AUTHOR

Vladimir S. Fonov - McConnell Brain Imaging Centre, Montreal Neurological Institute.

COPYRIGHTS

Copyright © 2009-2024 by Vladimir S. Fonov

SEE ALSO

itk_patch_grading , itk_patch_segmentation , itk_patch_morphology_mc