Example: Full-image and Masked Distributions

This example creates a deterministic intensity field with dark background, two foreground tissue groups, and a bright lesion. It then computes full-image and masked-foreground statistics through RITK’s public slice APIs.

The panels answer three separate questions:

1. Intensity field — what values are present and where?
2. Foreground mask — exactly which samples define the masked population?
3. Distribution comparison — how do the selected populations differ on one intensity axis?

Blue bars are the share of all image samples in each bin. Orange bars are the share of masked samples in the same bins. Each population is normalized by its own sample count, so bar heights compare distribution shape rather than mask size. Solid lines mark means, dotted lines mark medians, and the narrow bands at the base mark interquartile ranges.

The numeric table makes the change measurable. The full-image median remains in the dark background peak, while the masked median lies in foreground tissue. This is why an empty-mask fallback would be misleading: full-image and masked statistics are not interchangeable views of one population.

Source and command

Source: crates/ritk-statistics/examples/book_descriptive_statistics.rs

cargo run -p ritk-statistics --example book_descriptive_statistics -- \
  docs/book/figures/descriptive_statistics.svg

The example fails before writing the figure if:

• RITK’s min, max, mean, standard deviation, or floor-rank quartiles disagree with an independent full-sort reference;
• either histogram total differs from its source population;
• the image or mask violates the finite-input contract; or
• the phantom does not produce a large enough mean and median separation to make the comparison visually meaningful.

Adapt the workflow

extern crate ritk_statistics;

use ritk_statistics::{
    compute_statistics_from_slice, histogram_from_slice, masked_statistics_from_slices,
};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let image_values = [0.0_f32, 40.0, 80.0, 120.0];
    let mask_values = [0.0_f32, 1.0, 1.0, 0.0];

    let full = compute_statistics_from_slice(&image_values, 0)?;
    let masked = masked_statistics_from_slices(&image_values, &mask_values, 0)?;
    let full_histogram = histogram_from_slice(&image_values, 0.0, 120.0, 4)?;

    assert_eq!(full.min, 0.0);
    assert_eq!(full.max, 120.0);
    assert_eq!(masked.min, 40.0);
    assert_eq!(masked.max, 80.0);
    assert_eq!(full_histogram.total(), image_values.len());
    Ok(())
}

Use identical histogram edges when comparing populations. Different ranges or bin counts can make the same data appear different and invalidate a visual comparison.