Intensity Transformations

Pointwise intensity remapping filters: windowing, rescaling, thresholding, sigmoid, arithmetic operations, and histogram equalization.

Design

All intensity filters operate on flat host buffers via substrate-agnostic pure functions. Each filter follows the extract → compute → reconstruct sequence through ritk-image::Image boundary. No Coeus tensor is constructed in the hot path.

Filter Families

• Windowing: IntensityWindowingFilter — maps input range to output range
• Rescaling: RescaleIntensityFilter — affine remap with saturation clipping
• Thresholding: ThresholdImageFilter — binary threshold (inside/outside)
• Sigmoid: SigmoidImageFilter — logistic intensity remap
• Arithmetic: Add, Subtract, Multiply, Divide — unary/binary ops
• Equalization: HistogramEqualizationFilter, AdaptiveHistogramEqualizationFilter
• Clamp/Shift-Scale: ClampImageFilter, ShiftScaleImageFilter

The windowing and rescaling example uses the RIRE CT fixture to show why a tissue-specific HU window is not equivalent to a global output rescale. Its histogram and labeled panels are the reference visual for this chapter.

Spatial Intensity Non-Uniformity

N4BiasFieldCorrectionFilter estimates a smooth multiplicative MRI bias field in log-intensity space with histogram sharpening and multi-resolution cubic B-spline fitting. It is a spatial correction rather than a pointwise remap; the N4 book example shows the source slice, the corrected slice, the relative change map, and the estimated field on the RIRE MR fixture.

Verification

Each filter is differentially tested against its Coeus-generic counterpart via assert_coeus_matches_coeus. Analytical oracles verify endpoint mapping and saturation behavior.