itk_patch_morphology_mc

Multi-channel patch-based segmentation.

itk_patch_morphology_mc <input1> [input2] ... [inputN] [options]

DESCRIPTION

itk_patch_morphology_mc performs multi-channel patch-based segmentation, extending the single-channel itk_patch_morphology method to use multiple input contrasts simultaneously. Each input channel provides complementary information (e.g., T1, T2, PD), and patches are compared across all channels jointly to improve segmentation accuracy.

Channel contributions can be controlled using the --weights option to assign relative importance to each input. The tool uses the same non-local means framework as itk_patch_morphology, with exponential weighting of patch similarity across all channels.

OPTIONS

--train data

Training library specification file containing multi-channel 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.

--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.

--output labels

Write the output label volume to the specified file.

--weights w1,w2,…

Comma-separated weights for each input channel.

--groups n

Number of training groups.

--threshold d

Threshold for minimum patch similarity.

EXAMPLES

# Multi-channel segmentation with T1 and T2
itk_patch_morphology_mc t1.mnc t2.mnc --train library.csv \
    --output labels.mnc --mask brain_mask.mnc

# With custom channel weights
itk_patch_morphology_mc t1.mnc t2.mnc pd.mnc --train library.csv \
    --output labels.mnc --weights 1.0,0.5,0.3

# Output probability maps
itk_patch_morphology_mc t1.mnc t2.mnc --train library.csv \
    --output labels.mnc --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_morphology , itk_patch_segmentation