Image Processing & Correction

Mask

The two-valued image — used to surface a property, or to suppress one

Definition
A mask is a binary image — a two-valued image where each pixel is either foregroundLoading... (typically 255 or 1) or backgroundLoading... (0). It's the simplest image representation that still says something useful about the world: it answers, per pixel, yes or no, with no comment on intensity. The same mask can be read two ways depending on context — as the property you want to surface ("these are the cells") or as the property you want to suppress ("these are the regions to ignore"). Most StrataQuest pipelines spend their middle stages making, transforming, and combining masks before any measurement happens.
Measurements Mask - Cellular
Video · Primary
Supporting
H&EDSPTCH
Video · Supporting
Guided workflow - nucl and cell measurements
Video · Supporting
Two states, that's it
On or off, no in-between
Where masks come from
Threshold, classify, detect
Where masks go
Morphology, logic, ROI
A mask is not a coded image
0 or 255, vs. 0, 1, 2, 3, …

What you do with a mask

A first-pass mask from a single threshold is rarely the final form you want. Real images carry layered structure — variation across the field, isolated extreme pixels, regions whose shape is almost but not quite what the analysis is asking about. The mask becomes useful through a sequence of small transformations that each surface a particular property.

The typical pattern is: produce a starting mask, then shape it. Shape means morphology — dilate, erode, open, close, fill holesLoading... — to express the geometry you want. Combine — using AND, OR, XORLoading... — to keep only the intersection of two masks, or the symmetric difference. Constrain — cut narrow bridgesLoading... between regions the analysis should treat as separate objects. Then label, then measure.

The Operations Editor is built around this rhythm. A typical Detection slot is two engines, sometimes three, chained: detect, shape, label.

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