RITK: Medical Image Processing and Registration
RITK is a Rust toolkit for loading medical images, preserving their physical geometry, applying ITK-style filters, evaluating similarity, and producing classical or differentiable registrations. The book is organized as a user-facing workflow:
- load a volume through ritk-io;
- inspect shape, spacing, origin, and direction;
- preprocess intensities and spatial structure;
- choose a same-modality or multi-modal metric;
- resample and validate the result; and
- write the output without losing the spatial contract.
The examples use real RITK APIs and committed fixtures where a dataset is needed. Synthetic examples are deterministic, small, and input-sensitive so they can generate figures in CI.
First runnable workflow
use coeus_core::SequentialBackend;
use ritk_filter::IntensityWindowingFilter;
use ritk_io::read_image_native;
let backend = SequentialBackend;
let input = read_image_native("volume.nii.gz")?;
let windowed = IntensityWindowingFilter::new(-160.0, 240.0, 0.0, 1.0)
.apply_native(&input, &backend)?;
println!("shape = {:?}, spacing = {:?}", windowed.shape(), windowed.spacing());The NativeImage alias is Image<f32, SequentialBackend, 3>. RITK stores voxels in [depth, row, column] order while origin, spacing, and direction describe the physical frame. Filters generally preserve that metadata; resampling and transforms are the deliberate exceptions.
Workspace map
| Crate | User-facing role |
|---|---|
| ritk-io | Format inference, native readers, native writers, and DICOM series handling |
| ritk-image | Coeus-backed image storage and physical-coordinate metadata |
| ritk-filter | Intensity, smoothing, edge, morphology, diffusion, and spatial filters |
| ritk-segmentation | Thresholding, labeling, seeded region growth, watershed, level sets, and consensus segmentation |
| ritk-registration | Metrics, transforms, classical registration, and differentiable registration |
| ritk-statistics | Histogram, similarity, overlap, and image-quality statistics |
| ritk-diffusion-scheme | Validated b-values, gradient directions, coordinate frames, and format metadata |
| ritk-diffusion | Regularized analytical Q-ball ODF estimation and evaluation |
| ritk-tractography | Deterministic bounded streamline integration into Gaia polylines |
| ritk-transform / ritk-interpolation | Transform parameterization and physical resampling |
Build the book and examples
# Run tests with the repository's bounded native-test runner
cargo nextest run -p ritk-filter
cargo nextest run -p ritk-segmentation
# Build docs
mdbook build docs/book
mdbook test docs/book
cargo build -p ritk-filter --examples
cargo build -p ritk-io --examples
cargo build -p ritk-codecs --examples
cargo build -p ritk-analyze --examples
cargo build -p ritk-registration --examples
cargo build -p ritk-segmentation --examples
cargo build -p ritk-diffusion --examples
For native tests use the repository’s configured cargo nextest command. Doctests use cargo test –doc. A figure is valid only after its generating example succeeds and the rendered artifact has been inspected.
How to read the chapters
-
Part I explains format boundaries and spatial-axis conventions.
-
Part II builds filtering and segmentation pipelines from intensity, spatial, morphology, diffusion, and seeded-label operations.
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Part III develops diffusion-MRI acquisition, Q-ball ODFs, and tractography.
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Part IV covers metrics, transforms, classical and differentiable registration, and post-registration validation.
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Part V explains backend dispatch, zero-copy boundaries, and measurements.
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Part VI maps the public RITK surface onto Coeus, Leto, and Moirai.