Example: Curvature Flow

Curvature flow evolves a scalar field according to mean curvature. Unlike ordinary isotropic diffusion, the update depends on local surface geometry and is intended to smooth small-scale boundary roughness while retaining larger features for longer.

let filtered = CurvatureFlowImageFilter::new(CurvatureFlowConfig {
    num_iterations: 5,
    time_step: 0.0625,
})
.apply_native(&input, &backend)?;

The explicit Euler stability bound for a three-dimensional unit grid is dt <= 1/6. RITK uses double-precision stencil arithmetic internally for the curvature numerator and writes the result back to the image scalar type. Spacing is read from the image metadata.

The complete pipeline source runs five steps and renders the result beside an absolute input-to-output change panel. The change panel is not a second contrast-adjusted image; it is a direct difference diagnostic.

Source and verification

Source: crates/ritk-filter/examples/book_processing_pipeline.rs

cargo run -p ritk-filter --example book_processing_pipeline -- \
  docs/book/figures/processing_pipeline.svg

Curvature-flow tests cover constant images, stability-sized steps, boundary stencils, iteration behavior, and native/generic parity.