itk_patch_grading

Patch-based non-local grading using SVM, NNLS, or exponential weighting.

itk_patch_grading <input> [output_labels] [options]

DESCRIPTION

itk_patch_grading performs patch-based grading of a MINC volume using a library of pre-segmented training images. The grading score at each voxel reflects how similar the local patch is to patches from training subjects in different diagnostic groups (e.g., healthy controls vs. patients).

Three weighting schemes are available: exponential (default), non-negative least squares (NNLS), and support vector machine (SVM). The method is based on the non-local means framework, where patches from the input image are compared to patches from the training library and weighted by similarity.

This tool is commonly used for patch-based morphological grading in neurodegeneration studies.

OPTIONS

--train data

Training library specification file containing image paths and labels.

--train2

Use a secondary training library format.

--nnls

Use non-negative least squares (NNLS) weighting.

--svm

Use support vector machine (SVM) weighting.

--exp

Use exponential weighting (default).

--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 grading.

--prob prefix

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

--groups n

Number of training groups.

EXAMPLES

# Patch-based grading with exponential weighting
itk_patch_grading input.mnc grading.mnc --train library.csv \
    --mask brain_mask.mnc

# Grading with SVM weighting
itk_patch_grading input.mnc grading.mnc --train library.csv \
    --svm --mask brain_mask.mnc --patch 2 --search 2

# Output classification and probability maps
itk_patch_grading input.mnc --train library.csv \
    --cls classification.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