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NIH Image is Wayne Rasband's historical image-processing program for the Macintosh, preceding ImageJ. Its official documentation describes image acquisition, editing, filtering, calibrated measurements, stacks, a macro language and plug-in extensions.^[ref][ref]

One concrete representation choice explains why software history matters for interpreting old results. The manual describes internal pixels as unsigned 8-bit values and import of 16-bit data by rescaling to 8 bits. It also documents the convention of displaying value 0 as white and 255 as black; screen appearance and stored numerical values are distinct.^[ref]

For an original chosen conversion model, suppose a high-bit-depth image uses values from 0 to 4095 and is mapped to 8 bits byThen $Q(1000)=Q(1001)=62$. Two distinct recorded values become identical, so no subsequent measurement of the converted image can recover their difference. This is a stated quantization model, not an assertion about an undocumented rounding detail in NIH Image.

Keep the original data and the scaling rule when comparing legacy measurements. Spatial calibration is a separate operation: changing a length-per-pixel setting does not restore discarded gray-value information. The manual's analysis workflow selects a region of interest, computes measurements and records results, making those representation and selection choices part of a reproducible calculation.^[ref]

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]