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Area from a calibrated pixel count assigns an area to a selected collection of complete image pixels. For $N$ selected pixels of calibrated widths $\Delta x$ and $\Delta y$,This rectangular-cell model permits unequal horizontal and vertical spacing. In ImageJ, area is reported in square pixels or calibrated square units according to the spatial calibration and measurement settings.^[ref]

For an original threshold example, let four stored gray values be 40, 90, 130, 200. A rule selecting values at least 128 retains two pixels. If $\Delta x=\Delta y=0.5\mu \mathrm{m}$,At $\Delta x=0.5\mu \mathrm{m}$ and $\Delta y=0.8\mu \mathrm{m}$, the same pixel selection instead represents $0.80\mu {\mathrm{m}}^2$. Pixel count alone is not an area in physical units.

For a binary selection mask $M_{ij}\in \{0,1\}$, the count is $N={\sum }_{ij}{M}_{ij}$. A fractional-coverage model would instead use fractions $0\le f_{ij}\le 1$ and $A=\Delta x\Delta y{\sum }_{ij}{f}_{ij}$; the two rules need not agree at boundaries. A region of interest and an image thresholding rule also select pixels for different reasons. State the selection and calibration before interpreting the result as an estimate of a specimen's geometric area. The numbers above are stipulated image data, not a physical specimen measurement.

A binary selection mask records which pixels belong to a chosen subset of an image. On a finite pixel grid $\Omega$, it is the indicator functionIt can represent an area region of interest, the result of image thresholding or a combination of selection rules. The mask describes membership; its entries need not equal the underlying image intensities.

For a selected pixel count $N>0$, the count and selected arithmetic mean areConsider the original arraysThe selected values are 0, 20 and 40, so $N=3$ and ${\bar{I}}_M=20$. The selected zero-valued pixel still contributes to the denominator. Replacing excluded pixels by zeros and averaging all six entries would instead give $60/6=10$: that asks a different question. An empty mask has $N=0$, so this selected-mean formula is undefined.

For rectangular pixels with calibrated dimensions $\Delta x=0.5\mathrm{mm}$ and $\Delta y=0.2\mathrm{mm}$, the complete-pixel area from a calibrated pixel count is $A=N\Delta x\Delta y=0.30\mathrm{m}{\mathrm{m}}^2$. These are stipulated array values and cell dimensions, not a measurement of an object.

A stored mask can use another two-value encoding. ImageJ's Create Mask command stores 255 inside an area selection and 0 outside, corresponding to $B_{ij}=255M_{ij}$ in this model.^[ref] Summing $B$ gives 765 in the example, rather than the selected count 3. Convert the encoding or count selected entries before interpreting a sum as an area.

Two normalized masks $M$ and $Q$ combine by set intersection through $M_{ij}{Q}_{ij}$. Their complement within the specified grid is $1-M_{ij}$. Record the foreground-background polarity, coordinate alignment and grid extent when transferring a mask between images.

A region of interest in image analysis is a specified subset of image coordinates on which an operation or measurement is restricted. It can be an area, a line or a set of points, so its geometry determines which measurements are meaningful. ImageJ provides rectangular, oval, polygonal, line and point selections; an image region of interest describes their concrete use in that program.^[ref]

For an original example, let an image beChoose the top row as the region of interest, so its binary selection mask is $M=\left(\begin{array}{ccc}1& 1& 1\\ 0& 0& 0\end{array}\right)$. The selected mean isSelecting the bottom row instead gives 8, whereas the whole-image mean is 5. The stored image is unchanged; the question asked of it has changed.

A fixed geometric selection differs from selecting all pixels above an intensity threshold. Their intersection can be used when the intended measurement is a thresholded subset inside a prescribed area. Save the geometry, coordinate convention and image or frame association with the result. A line selection calls for a length or sampled profile, not the same complete-pixel area from a calibrated pixel count used for an area selection.

Wayne Rasband developed NIH Image and ImageJ, tools that make quantitative image processing available through an interactive program and extensible code. The ImageJ project's biography records forty years at the US National Institutes of Health and retirement in 2010. It dates the start of NIH Image to 1987 and ImageJ to 1997, when Rasband explored implementing the earlier program in Java.^[ref]

This progression mattered because the same recorded digital image could become input to many explicit measurements. Selecting a region of interest, applying image thresholding and measuring an area are separate operations; each makes a choice that affects the result. ImageJ User Guide by Tiago Ferreira and Wayne Rasband records the program's image representations, selections, analysis commands and scripting facilities.^[ref]

For a small original measurement example, take four stored grayscale valuesSelect values at least 128, obtaining the binary image $(0,0,1,1)$. If each complete pixel represents a rectangle of calibrated width $\Delta x=0.5\mu \mathrm{m}$ and height $\Delta y=0.5\mu \mathrm{m}$, the area from a calibrated pixel count isThe chosen threshold determines which pixels contribute; the pixel pitch determines the physical area assigned to each. An attractive display alone supplies neither of these measurement choices. The calculation assumes complete selected pixel cells and a uniform rectangular calibration; it is not a measured specimen area.

Rasband's contribution also includes maintaining a community around the software: the project profile describes improvements supplied through user feedback, bug reports, code and documentation. That process connects a general algorithm to the concrete representations and measurement conventions needed for a repeatable analysis.^[ref]