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ImageJ is a Java program for viewing, processing and measuring image data. Its official feature description includes image stacks, selections, calibrated measurements, image thresholding, plugins and recorded macros.^[ref] Wayne Rasband and Tiago Ferreira's user guide documents how acquisition metadata and analysis settings affect those measurements.^[ref]

For an original numerical measurement example, suppose a selected $2\times 2$ image region of interest contains gray values 10, 20, 30, 40. Set square pixel pitch to $0.25\mu \mathrm{m}$. The selection has four pixels, so its calibrated area and mean image signal areChanging only the spatial calibration to $0.50\mu \mathrm{m}$ per pixel changes the reported area to $1.00\mu {\mathrm{m}}^2$ while the mean remains 25. Spatial calibration changes the coordinate interpretation, not the stored gray values. An image thresholding rule retaining values at least 25 selects two pixels, whose total calibrated area under the original scale is $0.125\mu {\mathrm{m}}^2$.

A reproducible analysis records the original image, selection, spatial calibration, threshold rule and measurement settings. The guide separates spatial Set Scale from intensity Calibrate, and warns that global calibration can affect other open images.^[ref] The arithmetic above is stipulated test data, not a screenshot measurement or a result from an unspecified plugin.

An image region of interest in ImageJ is an explicit image selection whose geometry and image or slice association are used by subsequent operations. This is the program-level realization of a region of interest; it is not necessarily an object identified automatically by image segmentation. The guide describes selecting, measuring, saving and restoring selections with the ROI Manager.^[ref]

For an original two-frame example, letA fixed selection of the left column has mean 20 in the first frame and 30 in the second. A rule selecting values at least 25 instead selects the lower row in $I_1$ and upper row in $I_2$; its mean is 35 in both frames. Thus a fixed geometric selection and a value-derived selection can produce different measurements on exactly the same frames.

A saved selection is not a copy of all underlying pixel data. Reusing it requires the corresponding images and a consistent coordinate interpretation. Record whether the same geometry is applied across an image stack or each frame receives a newly generated selection. The ROI Manager can retain slice associations, while its multi-measure operation can apply selections across slices; these are choices to specify, not evidence that an object has remained stationary.^[ref]

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.