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Overview

Animal studies contribute significantly to our understanding of human disease, and function as an established and essential step in the development of treatments and other therapeutic agents. These studies are conducted in the preclinical phase, preceding drug screening in human clinical trials. The aim of these studies is to determine efficacy, safety, dosing, and toxicity, as well as risk-benefit trades. There are many established, standardized methods implementing animal studies in life science and chemical analysis.

There are three broad life science experimental categories: in vitro, ex vivo, and in vivo. In vitro testing studies biological matter (cells or tissues) outside of a living organism, with experiments typically employed in a Petri dish. The benefit of in vitro studies is that they provide cellular and molecular analysis, alongside development of cell lines for future studies.

However, its biggest limitation is the inability to replicate the naturally occurring environment of the organism. To combat this, there is ongoing development in in vitro techniques, such as organoid and organ-on-a-chip, that show promise in their ability to compliment, and in some cases supersede, cell cultures and animal model testing.

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The following table contains reference information for Windows Image Acquisition (WIA) drivers and for Still Imaging (STI) drivers. These drivers control devices, including scanners and cameras, that capture still images. For more information about these drivers, see Windows Image Acquisition Drivers and Still Image Drivers. For each of those devices there should be built-in drivers available - the first device appears to be the webcam and the second device appears to Bluetooth related. In the Device Manage can you right-click each device and select 'Uninstall' and then uninstall the devices and then restart the computer and let Windows reinstall the devices.

By contrast, ex vivo is used to study specific organ and tissue function removed from other bodily stimuli, conducted under controlled conditions. This process works by artificially mimicking body function outside of an organism while studying an extracted organ/tissue of interest, as shown in Figure 1A.

If there is evidence that the experimental strategy works in in vitro/ex vivo experiments, studies may progress to in vivo experiments to gather additional information and verification. In vivo experiments employ a whole, living organism as opposed to ex vivo tissue or in vitro controlled, cellular based experiments.

Animal testing and clinical trials are two forms of in vivo research. In vivo testing is often required alongside in vitro to observe the effects of an experimental process throughout an entire organism. Mice, rabbits, guinea pigs and monkeys (non-human primates) are common animals used to evaluate potential treatments in preclinical in vivo experiments, as shown in Figure 1B. In vivo imaging is therefore essential to determine the path forward in critical life science research.

Advances in non-invasivein vivo imaging techniques have enabled the effective use of animal models in increasing numbers of preclinical drug research and development programs. These advances have also enabled researchers to conduct long-term longitudinal experiments, increasing the understanding of animal models and decreasing the time to reach quantitative assessments. This has the further advantage of reducing the number of animal specimens required to complete an in vivo study.

Imaging Modalities

There are several imaging modalities that are well established for small animal in vivo imaging: optical imaging (OI), magnetic resonance imaging (MRI), computed tomography (CT), micro-computed tomography (μCT), singlephoton emission tomography (SPECT), scanning electron microscope (SEM), ultrasound (US), and positron-emission tomography (PET).

To a large extent, these different modalities are complementary, rather than competitive. To promote the complimentary approach, some suppliers implement multiple modalities within the same instrument, with others providing ease of sample transfer between different imaging modalities. For example, the combination of OI and µCT imaging has been implemented in a number of commercially available in vivo imaging systems

Optical Imaging

Optical imaging (OI) was first used preclinically over two decades ago with the introduction of in vivo imaging. OI is advantageous as it is non-invasive, allowing for live imaging of animals. This allows the technique to deliver fast, longitudinal, accurate, real-time and quantitative assessments of drug efficacy.

A typical small animal in vivo imaging system consists of a “dark box”, which encases the camera, optics, and the animal subjects. The number of animals being imaged within a dark box varies from single subjects up to as many as ten. Also, inside the dark box is a collection of accessories, such as an anesthesia manifold and animal tray, which contribute to the experimental set-up. In addition to the equipment is the system electronics and typically a camera chiller, separated from the main “dark box” [1].

OI is a technique that utilizes the difference in optical scattering properties of individual tissues for image formation. Different OI imaging techniques provide complementary information regarding structure, metabolism, and physiology of an animal. Information provided by the individual techniques can be synergized by an image registration mechanism, which combines the images obtained from each technique.

Bioluminescence imaging (BLI) and fluorescence imaging (FLI) are the predominant imaging techniques used in OI. BLI is the result of an enzymatic reaction, with FLI occurring when incident light of one wavelength is absorbed by a fluorescent probe, and then emitted with a different (usually longer) wavelength. This means that FLI requires a light source to excite a fluorescent molecule (fluorophore), which then emits photons creating an image [2]. Comparatively, BLI uses light-emitting proteins (luciferases) found naturally in insects, bacteria, and plants for cell labeling in vivo [3].

Although not directly translational to the clinic, these techniques have become the primary resource for imaging of small animals in the research environment, due to their ease of use, flexibility, inexpensive nature and the capability for rapid image collection. These techniques are also advantageous as some of the models and assays developed prior to preclinical studies, within tissue culture, can be directly transferred into small animals. The main animal model used for whole animal molecular OI is the mouse, with “murine” studies performed as standard. Rat imaging is also feasible, alongside studies of larger animals (such as rabbit or non-human primate) via superficial imaging.

Optical imaging has advanced as a modality due to more sensitive cameras, greater processing power and data storage capacity, alongside more sophisticated algorithms. Correlating with other imaging modalities has become simpler, and in some cases, seamless through the use of common equipment, or via shuttles between instruments that allow for coregistration of fiducial markings. This allows complementary data to be garnered from the same animals simultaneously or over time [1]

Interaction of Wavelengths within Biological Tissue

In vivo applications, especially those which use fluorophores, must consider the properties of the excitation light source to prevent unwanted tissue damage. For example, deep blue or ultraviolet (UV) can result in tissue trauma, whereas excitation in the infrared (IR) can cause tissue heating.

Another issue is that certain wavelengths penetrate tissue poorly, with visible blue/green wavelengths having low penetration depth, limiting their use to very small animals or surface imaging [4]. Alongside this consideration, components within the animal, such as hemoglobin and related molecules, naturally fluoresce in the visible blue/green range. This is known as tissue autofluorescence. Therefore, the optimal excitation wavelength of most conventional fluorophores is in the deep red or near infrared range, commonly referred to as NIR-I window, due to adequate tissue penetration and low tissue autofluorescence. Figure 2 below shows spectroscopic characterized blood and tissue based on differences in the optical absorption and scattering properties.

Different wavelengths scatter and penetrate tissue differently due to the scattering coefficient of each tissue type. The degree of scattering within biological tissue (μ’s (λ)) is described by the function:

μ’s (λ)=αλ-ω

with α relating to the concentration of scattering centers in tissue and the ratio between the refraction of the surrounding medium and the refraction of the scattering. ω is the wave exponent, and relates to the behavior of the scattering coefficient of the tissue, with a value usually between 0.2 ~ 4 in biological tissue, and λ relates to the wavelength of incident light [5,6].

Therefore, the longer the wavelength used, the less scattering occurs with deeper penetration into the tissue. Wavelengths within the NIR-I window (760-900 nm) are advantageous as they are poorly absorbed by water and hemoglobin within the tissue, unlike the visible range. This causes the tissue to become transparent (as it is predominately comprised of water and hemoglobin) allowing for NIR-I fluorophores to be detected deep within the tissue. However, these excitation wavelengths also cause autofluorescence within biological tissue, contaminating data acquisition [7]. In vivo imaging within the NIR-II wavelength range has further advantages. As the NIR-II wavelength window encompasses longer wavelengths (1000-1700 nm) there is less scattering within the biological tissue.

Although there is increased absorption from various biological tissues, the reduced scattering alongside a reduction in tissue autofluorescence contributes to higher spatial resolution than NIR-I imaging. Both these wavelength windows require either a HiRho CCD sensor or an InGaAs sensor dependent on wavelength range and resolution required. Figure 3 shows multiple imaging modalities and the imaging parameters they are able to achieve in vivo.

Imaging Parameters and Performance

There are a number of imaging parameters that drive effective image acquisition, primarily detector sensitivity, spatial and temporal resolution, and cumulative noise. Most high-performance in vivo imaging systems use either a charge coupled device (CCD) or an InGaAs 2-D array detector, dependent on the wavelength of the signal being emitted. Specifics of these parameters can be seen in Table 1. Usually in vivo fluorophores produce a faint signal, due to the requirement for longer exposure, detectors with low noise are required. Therefore, most in vivo imaging systems employ deeply cooled cameras [8].

Detector Sensitivity

Sensitivity can be defined as the “photon capture” capability of the system. Quantum efficiency (QE) is a function of the quantitative ability of the system to convert photons to electrons, producing a digital image. System QE is often simplified to describe only the QE of the camera detector, however detector QE, combined with the transmission of optics and filters, will determine the percentage of signal collected by the system.

Sensitivity is also impacted by the wavelength of the photons emitted, with each detector having an optimal QE that corresponds to a specific wavelength range. CCD detectors are sensitive between 350 and 1000 nanometers (nm), although QE for standard silicon CCD detectors drops drastically from 800 nm. Typical CCD detectors have peak QE in the mid-visible range.

Therefore, most in vivo systems require a back thinned, or back-side illuminated (BSI) CCD. This fabrication method increases the absorption of photons resulting in a much higher QE than a front-side illuminated CCD. InGaAs detectors typically cover the wavelength range 900 – 1700 nm, with some reaching as far as 2.5 micron. This range is referred to as short wave infrared, or SWIR, and encompasses the NIR-II window. Fluorescence imaging in the short-wave infrared (SWIR) regime is generating substantial interest in the biomedical community as explained above.

Typical QE of CCD and InGaAs detectors is shown in Figure 4. Detectors fabricated with high resistivity bulk silicon (HiRho) have higher QE in the near infrared and produce less fringing pattern in the image. Fringing is caused by the constructive interference of monochromatic light when reflected within the CCD sensor. These detectors provide imaging capability from 900-1050 nm, in the otherwise limited performance ranges of CCD and InGaAs. This sensitivity corresponds to the NIR-I window. InGaAs detectors have very consistent QE across the range from 1000 nm to 1600 nm.

Spatial Resolution

Spatial resolution is the smallest object that a sensor can resolve. The sample structure and emission intensity determine the spatial resolution required for accurate data acquisition, with sensor size also playing a factor. Sensor size and optics are important as they establish the field-of-view and the total signal collected from the sample. These parameters are impacted by the performance, economics and current availability of cameras and optics. Pixel count is also a factor as it will generally determine feature resolution. Smaller pixels allow for higher resolution, but at the cost of lower dynamic range as they are collecting less signal for a given noise level than a larger pixel would. A way to combat this limitation is through pixel binning, in which signal from multiple pixels are cumulatively added into a single pixel. This increases sensitivity but decreases resolution.

Detectors can have a wide range of resolutions. Taking a CCD detector as an example, spatial resolution can be 320 x 240 to as large as 16 megapixel. In optical in vivo systems, typical CCD formats range in resolution from 512 x 512 up to 2048 x 2048. This resolution is achievable through pixel sizes typically between 10 – 15 μm. These pixels accumulate to create a large pixel array size of approximately 30 x 30 mm. Three of the most common detectors used in in vivo OI applications are manufactured by Teledyne e2v, shown in Table 2. These detectors have typical in vivo detector characteristics, including spatial resolution, low noise and high sensitivity.

Noise Reduction Via Cooling

Low-light imaging and spectroscopy applications, such as in vivo imaging, rely on highly sensitive silicon or InGaAs scientific detectors. To ensure high sensitivity, it is necessary to cool the cameras to minimize thermally generated noise. By cooling the system, to often between -60^oC and -100^oC, and reducing noise, integration times can range from several minutes to multiple hours. For example, dark current is thermal noise generated by the camera in absence of light. By using a vacuum seal to ensure cooling of the system, this background noise is significantly reduced.

Another example is readout noise, generated by the camera electronics such as the output amplifier. This noise is the limiting factor for low light images. By cooling the CCD, the transfer rate will become slower while increasing the charge transfer efficiency, overall reducing the readout noise. In InGaAs cameras, dark current can be as significant as readout noise, often being the limiting factor in low light applications. As well as dark current, InGaAs cameras are also impacted by ambient background radiation. For this reason, InGaAs cameras must cool the sensor as well as the optical path to reduce these noise contributions.

Although a number of cooling technologies (e.g., thermoelectric, cryogenic, Joule-Thomson) have been used in scientific cameras over the years, thermoelectric (TEC), otherwise known as Peltier cooling, has proven to be the most attractive due to its maintenance-free and reliable operation. Achieving the deepest cooling using a TEC requires multiple parameters, such as an ultra-high vacuum (UHV) environment around the sensor, non-outgassing materials, no epoxies, and permanent, all-metal, hermetic seals. To maximize light throughput, a single-window vacuum design is best. This design ensures that the vacuum window is the only optical surface encountered by incident photons before they reach the detection surface.

However, each uncoated optical surface of the vacuum window can have up to 4% transmission loss. When imaging in low-light applications, this loss can result in a significant reduction of signal-to-noise ratio. Moreover, any light reflected inside the system can lead to glare and fringing, especially when used with coherent illumination. To solve this limitation, anti-reflective (AR) coatings are applied to the window in the optical path.

This results in a reduction of transmission loss to <1%, with some coatings achieving <0.5%. To optimize the design, an efficient heat exchanger should be incorporated. The heat exchanger allows for high thermal dissipation, alongside intelligent controls to achieve high reliability and temperature stability. The figure below shows a typical high-performance vacuum assembly, and the positive effect of CCD cooling on dark current noise.

Summary

In vivo imaging is an essential part of therapeutic agent development, providing the key preclinical data for determining success rate of human clinical trials. Whole animal imaging heavily relies on fluorophores introduced into the system to measure the effectiveness of a potential agent, alongside efficacy, safety, dosing and toxicity.

Limitations of in vivo imaging relate to spatial resolution, wavelength penetration depth and autofluorescence of biological tissue. Fluorophores within the NIR-I and NIR-II window have been developed to reduce these limitations. NIR-I/-II fluorophores increase spatial resolution through reduced tissue scattering, reduced tissue absorbance and reduced tissue autofluorescence.

Fluorophores within the visible range are still commonly used for in vivo imaging, as there are multiple, medically approved fluorophores within this range that are low in toxicity, available for clinical use. For visible range fluorophores, CCD sensors are commonly used for optical imaging, with NIR-I/-II fluorophores requiring either HiRho CCD or InGaAs sensors. These sensors have high QE from 750 – 1050 nm and 1000 – 1600 nm respectively, so are optimal for NIR-I/-II imaging. To ensure the lowest possible noise, these sensors need to be deeply cooled, especially within low-light imaging typical of in vivo experiments.

References

1. Recent Developments in Small Animal Imaging, Josh P. Roberts, February 20, 2020. https://www.biocompare.com/Editorial-Articles/560606-Recent-Developments-in-Small-Animal-Imaging/
2. Studwell, Anna J, and Darrell N Kotton. “A shift from cell cultures to creatures: in vivo imaging of small animals in experimental regenerative medicine.” Molecular therapy : the journal of the American Society of Gene Therapy vol. 19,11 (2011): 1933-41. doi:10.1038/mt.2011.19
3. Yanping Sun, Herbert B. Newton, Chapter 36 – Neuroimaging of Brain Tumors in Animal Models of Central Nervous System Cancer, Editor(s): Herbert B. Newton, Handbook of Neuro-Oncology Neuroimaging (Second Edition), Academic Press, 2016
4. New Strategies for Fluorescent Probe Design in Medical Diagnostic Imaging Hisataka Kobayashi*†, Mikako Ogawa†, Raphael Alford†, Peter L. Choyke†, and Yasuteru Urano‡ Chemical Review, 2010, 110, 5, 2620–2640, Publication Date: December 15, 2009, https://doi.org/10.1021/cr900263j
5. Fan, Yong & Zhang, Fan. (2019). A New Generation of NIR‐II Probes: Lanthanide‐Based Nanocrystals for Bioimaging and Biosensing. Advanced Optical Materials. 10.1002/adom.201801417.
6. Bashkatov, A.N., Genina, E.A., Kozintseva, M.D., Kochubei, V.I., Gorodkov, S. Yu., and Tuchin, V.V. Optical properties of peritoneal biological tissues in the spectral range of 350-2500 nm. Optics and Spectroscopy,120, 1-8, 2016.
7. Zhu, S. Kwon, J. C. Rasmussen, M. Litorja and E. M. Sevick-Muraca, “Comparison of NIR Versus SWIR Fluorescence Image Device Performance Using Working Standards Calibrated With SI Units,” in IEEE Transactions on Medical Imaging, vol. 39, no. 4, pp. 944-951, April 2020, doi: 10.1109/TMI.2019.2937760.
8. Remi, Sebastian., Laser Focus World Article, 28/01/2020, https://www.laserfocusworld.com/test-measurement/spectroscopy/article/14074088/superdeepdepletion-ccds-optimize-bioraman-spectroscopy?utm_source=LFW+Test+%26+Measurement&utm_medium=email&utm_campaign=CPS200520051&amp;o_eid=4103H7594489B5Y&rdx.ident%5Bpull%5D=omeda%7C4103H7594489B5Y&oly_enc_id=4103H7594489B5Y

Further Reading

NIR-II Probes for In vivo Imaging

Overview of the most common NIR-II probes currently used within in vitro, ex vivo and in vivo studies, including quantum dots, single-walled carbon nanotubes and rare earth nanoparticles.

InGaAs Cameras for Drug Discovery and Small Animal Research

Brief overview of how the SWIR wavelength range can be utilized for small animal imaging and drug discovery.

Carbon Nanotube Sensors for Early Disease Detection

Overview of the development of novel nanomaterials which make it easier to identify disease biomarkers within the body.

Adaptive thresholding

Advanced 2D adaptive thresholding estimates the background gray level in a window area around each pixel. The difference between the actual pixel value and the background is then compared to the adaptive settings to determine if a pixel is thresholded as a black or a white pixel.

Additive colors

The additive primary colors are red, green and blue (RGB). These additive primaries represent the three main components of white light. Used individually or together, these three colors of light can be mixed to create nearly all colors. When these three primary colors are mixed in equal parts they produce white. Additive color is used in scanners and computer displays.

ADL+ error diffusion halftoning

Image processing that supports visibility of graytones in printed output by adding toned shades of gray in regions between black and white. Carried out as a segment of dual 2D-adaptive enhancement processing in copy modes.

ALE – Accuracy lens enhancement

Accuracy lens enhancement (ALE) is an electronic correction of spherical errors in CCD based camera scanning systems.

When looking at pixels across the range of a camera, the pixels tend to be more elliptical at the outside edges of the lens and more round in the middle of the lens. This anomaly is known as a spherical lens error and can introduce inaccuracies in the scanning system that can vary quite substantially between different points along the scan line.

Most manufactures typically state a +-0.1% accuracy of the scanner between the two outermost end-points of the scan line. However, when measuring between two points that do not fall across the entire scan line, it is not unusual to see variations of up to +- 0.5% or even higher. This is naturally unacceptable in demanding environments and markets such as GIS, which need a stable and well-defined maximum error of 0.1% or less.

ALE solves this problem by a process to electronically correct the spherical errors in the scanner and maintain a stable maximum error across any two points of less than 0.05% ± 1 pixel.

ATAC – Automatic thickness adjustment control

A special technology that allows the scanner pressure platen to be raised to accommodate thick originals and then lowered – both actions performed by pressing a key from the operators panel. Sensors in the platen detect when perfect pressure is applied to the the original and automatically stop the downwards motion of the platen so it rests on the original with an optimal grip.

Bitmap (.bmp)

An image format made from a matrix of individual pixels.

Bitmapped image

A bitmapped image is a computer file representing a line-art image that was scanned with a scanner. Refers to the pattern (map) of bits that are either black or white.

Black level

The black level is a setting in scan programs used to change dark graytone colors to true black. For example, when copying a brochure with a mixture of text and pictures, the text will often be digitized to a color that we may see as black but really is a dark graytone. When the printer digests this graytone data, it will print the original’s text with a halftone pattern, meaning scattered dots instead of solid black. By increasing the black level value, the text can be copied in real black and will therefore appear clearer.

Black Point Adjustment

An adjustment that will determine the amount of shadow detail in an image. It is considered proper to set the black point so that the darkest part of an image will only just have zero detail.

Blueprint

A process of photographic printing used mainly for copying architectural and mechanical drawings; produces blue lines on a white/bluish background.

Blur

The averaging of pixel elements.

Brightness adjustment

An adjustment on a scanner that allows the user to compensate for a light or dark original.

Calibration

Adjusting a device so that it performs in accordance with an established standard. Scanner calibration is minimizing color deviation between scanned ANSI IT8 reference color patches and the known color reference values. Generally, calibration is the process of setting a device to known color conditions – stabilizing the device to a known and quantifiable state. Calibration is commonly done with devices that change color frequently, such as monitors (phosphors lose brightness over time), scanners (light changes) and printers (proofers and other digital printing devices can change output when colorant or paper stock is changed).

CALS

Computer-aided acquisition and logistics support (CALS) standard is a U.S. Defense Department and industry initiative that addresses the design, manufacture, and support issues of generation, access, management, and use of technical data in digital form.

CCD

Charge coupled device (CCD) is the image sensor in the scanner that converts light to voltages. These voltages are converted by the scanner into the image.

CCITT Group 3

Standard runlength compression format used with FAX transmission. It utilizes modified Huffman coding to further compress the runlength numbers. Most scanner file formats are dialects of this format.

CCITT Group 4

Two-dimensional compression format, giving very compact image files. Standardized by CALS (MIL 28002) and ISO-ODA for drawing archival and interchange.

CIE

Centre Internationale d’Eclairage (CIE) is an international organization that establishes methods for measuring color. These color standards for colormetric measurements are internationally accepted specifications that define color values mathematically. The first color space model, the CIE xyz, was developed in 1931. CIE defines color as a combination of three axes: x, y,and z. The two color spaces released in 1978 are CIELAB and CIELUV. The goal was to provide an accurate and uniform reference of visual perception.

CIELAB

A device-independent color space specified by CIE (see above), used in modern color management software to facilitate conversion of data from a scanner to a display, or from a display to an output device.

CMYK

The subtractive printing colors: cyan, magenta, yellow, black.

Color balance

The visual effect of an image when the amount of each color and the overall amount of color are balanced.

Color bit depth

The simplest pixel has two options: black or white. (A pixel with two choices is known as a 1-bit image, or two raised to the power of one). Adding more bit information increases the number of color options. The number of potential color options for a pixel is called color bit depth. For example a 4-bit pixel would have 16 color options, and an 8-bit pixel would have 256 color options, while a 24-bit pixel would have 16,777,216 color options.

Color cast

An image is said to have a color cast if its colors are not true. A color cast will usually be described by stating the particular color predominant in the image, e.g., the grass appears to have a red color cast.

Color correction

Used to improve the color rendition. Correcting for, and eliminating an unwanted color cast.

Color management system

Color management system (CMS) software increases the accuracy of color interchange between scanners, displays and printers based on profiles for each device. The CMS is a layer of software resident on the computer that negotiates color reproduction between the application and color devices. The CMS performs the color transformations necessary to exchange accurate color between diverse devices. The color manager needs access to characterization data for the device. The format and content of such device profiles is standardized by the International Color Consortium (ICC.)

Color separation

Process of separating colors in an image into primary color components for printing. Converting an RGB color image into CMYK color image. Color separation is a technical function during which critical settings such as GCR, black ink limit and total ink limit are applied to the image.

Color space

A color space is a particular language used to describe color. Examples of color spaces are: RGB, CMYK, HSV, CIELAB.

Contrast

The difference between the lightest and darkest significant areas in a picture. A picture with high contrast has nearly white areas and nearly black areas with sharp changes in brightness between them. The picture seems dominated by stark light and dark tones.

Density units

Photographers and printers measure transmission in base-10 logarithmic density units, where transmission of unity corresponds to a density of 0, transmission of 0.1 corresponds to a density of 1, transmission of 0.01 corresponds to a density of 2, and so on.

Density

The light-stopping ability of a film. Density is inversely proportional to the amount of light reflected or transmitted by an image.

Device dependent color space

Examples:

• RGB: A device dependent color space, e.g., the same scan file will appear different when viewed on different computer displays
• CIELAB: A device independent color space is one in which color values are absolute, e.g., defined by CIE standard. CIELAB is the central color space in color management systems (CMS) and is used to translate between different device dependent color spaces such as scanner RGB and display RGB

Device profile

A file used as part of a color management system (CMS). A device profile contains information about the characteristics of a scanner, computer display or printer. The format for device profiles (Win95, Colorsync, etc.) is standardized by ICC (International Color Consortium).

DIP – Digital image processor

Hardware embedded function that does image enhancement in real-time while scanning.

Dither

To use patterns of different colored pixels to create blended colors; or, to use dots of different sizes to simulate grayscale images (see below).

Dithering

A printing or display device may have only a small number of grayscale or color values for each device pixel. However, if the viewer is sufficiently distant from the printed page or display, the value of neighboring pixels can be set so that the viewer’s eye integrates several pixels to achieve an apparent improvement in the number of levels or colors that can be reproduced.

Dots per inch (dpi)

A measure of dots in a square inch where the individual element is a round dot on the printed page.

DPI (dots per inch)

Equivalent to PPI pixels per inch. An expression of resolution of a scanned image.

Drag

Press the left mouse button and move the mouse while keeping the button pressed. When the desired action is completed, release the mouse button. Drag refers to an action sequence (mouse down, mouse move, mouse up), such as “Drag the button in the scroll bar …”

DSP – Digital signal processor

A digital signal processor does image enhancement in real-time while scanning.

Dual 2D-adaptive enhancement

Enhancement processing on the foreground and background separately. Processing is performed on-the-fly. The separate enhancement processes are simultaneously performed on different drawing aspects.

Dynamic range

A measurement of scanner quality; the density difference between highlights and shadows.

Edit

Modify an entry using standard Windows text-editing techniques.

Emulsion

The light sensitive silver, coated on the clear acetate film base, that forms the photograph when a picture is taken and the film is developed.

Equalizing

Distributing all color or tone equally along a density range.

File format (image)

The format in which a scanned picture is saved. Many programs can insert or import a picture from a file, if it is saved in a file format that the program supports. Common file formats include TIFF (Tagged Image File Format), BMP (Windows bitmap), JPEG (Joint Photograph Expert Group), and FPX (FlashPix format).

Flip horizontal

To flip the picture left/right.

Foreground

Foreground when scanning raster data (black and white, or monochrome data) refers to the pixels that represent data of interest (background refers to everything else). Typically, lines and shapes are represented by black pixels (foreground) and empty space is represented by white pixels (background). When scanning grayscale data, background means the gray level of a region of pixels that surrounds some desired foreground data.

Gamma adjustment

An adjustment that makes the tone distribution lighter or darker in an image.

Gamut

The color range scanable, printable or displayable by a device; e.g., if some of the displayable colors are outside of the gamut of the printer they cannot be printed.

Gamut transformation

Color management system (CMS) function, where out-of-gamut colors are converted to colors within the gamut of the targeted device, e.g., a printer.

GCR

Gray component replacement. A color separation setting used on color photographs where cyan, magenta and yellow inks are replaced by black ink (in a balance that would yield a gray value). The advantages are a reduction in overall ink usage and some increase in image detail.

Grayscale

A term for a black and white photographic image or a scanner setting. Refers to the range of 256 gray tones that make up the image.

Halftoning

The processes of offset printing and laser printing are intrinsically bilevel. However, these devices can reproduce a range of tone levels by halftoning; e.g., an array of widely spaced dots produces the perception of light gray, and an array of tightly spaced dots produces dark gray. Halftone dots are usually placed in a regular grid. In color printing it is conventional to use cyan, magenta, yellow and black grids that have exactly the same dot pitch but different carefully-chosen screen angles.

Highlights

The lightest part of a picture – reproduced as white on the screen or when printed.

Histogram

A bar graph representing the statistical distribution of graytones or colors in an image. Each column represents the number of pixels at that gray level or color.

HLS

A color space with the three variables of hue, lightness and saturation.

HSV

A color space with the three variables of hue, saturation and value.

• Hue means color (as in the color wheel)
• Saturation is an indication relating to the richness or vibrancy of the color
• Value is a term best related to the intensity of light illuminating the object

Hue

A named color. In discussions of color that relate to photography, scanning, and printing, six hues are especially important: red, yellow, green, cyan, blue, and magenta. These hues make up every color we can see, and are the designated hues on color wheels.

Hue

A measurement of color that can be related by pointing towards a certain color on the color wheel. Hue indicates the relative redness, blueness, greenness, yellowness, etc., of a color.

ICC

The International Color Consortium (ICC) was formed to address the need for a common color framework. The ICC has developed a standard device profile that contains information about how various devices render color. This concept is supported by Apple (Colorsync), Microsoft for Windows 95, Sun for Solaris, and by Silicon Graphics for Irix.

Image editor

A program used to edit pictures to change colors, increase detail, scale or otherwise alter the picture.

Indexed color

Indexed color (or pseudo-color) is the provision of a relatively small number, say 256, of discrete colors in a colormap or palette. For each pixel in the image, the index number of a color is then stored. When retrieving the image, a lookup table uses the index to retrieve red, green and blue components that are then sent to the display. In graphic file formats such as PCX of TIFF, an indexed color image is accompanied by its colormap.

Interpolation

Using the interpolation method of resampling, interpolation generates values for points in between the actual pixels by looking at the surrounding colors or intensities. In a scanner, resolution is increased beyond the actual number of CCD cells. As each line of pixel data arrives from the cameras, new interpolated pixels are added between original pixels. The added pixels enhance line edge definition.

JPEG – Joint photographic expert group

A compressed file format for images. JPEG images feature small file size and speed, but lower quality than other formats.

JPEG compression

A method to save storage space by compressing files. JPEG achieves a high degree of compression by discarding non-important picture detail.

Lossless compression

File compression and subsequent decompression without any loss of data.

Lossy compression

File compression that will compress data to a high degree. When subsequently decompressed, data will have been lost.

LZW compression

Method of lossless compression used with many file formats. Developed by Lempel, Zev and Welch.

Midtones

The most important part of a picture between black (shadows) and white (highlights).

Negative

A reversed photographic image used to produce a positive print or a scanned image.

.NET architecture

.NET architecture is a solution for scanning across local networks.

What does it do?

• Enables sharing a scanner on a network
• Enables scanning to a designated scan folder on another computer

.NET architecture allows a a scanner to scan to a client PC in a single coherent and secure process. The client does not need to expose or share his local hard disk as the system can be set up for authorized transfer to the client.

Example of usage – a company that needs to create digital documents of its drawing archive, can send the drawings to a service bureau that scans all the documents directly to the client (company) file server allowing immediate feedback from the client and prevents digital distribution of confidential documents outside the client company.

.NET Architecture also allows users in a company to use a scanner from their own PC workstations although the scanner is physically placed elsewhere. It only need to be on the same LAN. In this way a single scanner is “shared” throughout the company.

Noise

A term used to describe the occurrence of pixels that contain random colors within an image.

Original

The paper, negative, slide, or film to be scanned.

Palette

The set of colors available for an image.

PICT

A graphics file format for pictures introduced on the original Apple Macintosh computer.

Pixels

The word pixel is a combination of the two words picture and element. It is the smallest building block within a scanned line-art or photographic image. A pixel is the small square picture element that is filled with a color, black or white. The value of a pixel depends on the luminance of the area, and is either a single bit for a black and white image, or multi-bit for a color or graytone image. Pixels come in various sizes and their size is expressed in terms of resolution. Resolution is measured in pixels per inch (ppi) or the equivalent dots per inch (DPI.)

Pixels per inch (ppi)

A measurement of resolution for scanners, where the individual element is a square picture element (see pixel).

PostScript

A computer language developed by Adobe® Systems, Inc. for printing text, graphics, and scanned images. PostScript is a vector format that can include scanned bitmapped images.

Raster file

Also called raster image or bitmapped image. A picture composed of individual dots (picture elements, pixels) the way a scanner perceives it. The rows in a high-resolution raster file typically contain 200 or 300 dots per horizontal inch of the original drawing, and there are typically 200 or 300 rows per vertical inch. As each of these dots is defined by location, and by whether it is on or off, raster images generally result in large data files.

Resolution

A measure of how many pixels per inch (ppi) are scanned.

Resolution defines the level of detail that can be captured or shown by a scanner, display, or output device. Generally, more pixels per inch means more detail in the picture and a larger file when saved.

• Scanners resolution is defined by the number of dots (pixels) per inch (DPI) that can be captured horizontally and vertically, e.g. 300 DPI equals 90,000 pixels per square inch
• Screen resolutions are normally 72 pixels per inch of screen. Additional detail is thrown away by the screen display driver
• Printer resolution scans requires 150 dots per inch and above for good results on the printed output

Resolution of a scanner

Expressed as DPI (dots per inch) or the equivalent ppi (pixels per inch). The higher the resolution of a scanner, the smoother the scanned images.

RGB – Red, green, blue

These additive primary colors are the basic elements of white light. By mixing them on a computer monitor or in a scanned image file, other colors can be created. For instance, red and green produces yellow, and equal amounts of all three produce gray.

RIP – Raster image processor

A RIP is a special software that converts scanned images into a color dithered (halftone) image that can be output directly. An image must be ‘ripped’ before it can be output on a CMYK device, e.g., an inkjet printer.

Rotate

To turn the picture right (clockwise) or left (counterclockwise) from the orientation in which it was scanned.

Runlength encoding

A method of compressing raster or bitmap data by representing “runs” of white or black dots along a scanned line as the number of dots in each run. Many variations of this scheme exist, with varying compression efficiency. Typically, runlength compression formats yield a file 20-25% the size of an uncompressed file.

Saturation

The level of colorfulness of the picture. A picture with high saturation has vivid color. A black and white picture has zero saturation. The purity of a color or the degree to which it is diluted with white light. Red is a highly saturated color. Pink is a diluted red (has lower saturation).

Saturation is one attribute of color in the color space called HSV (hue, saturation, value). Saturation is a characteristic indicating the vibrancy or intensity of a hue. A color with high saturation will appear more intense than the same color with less.

Scale

To reduce or enlarge the size of a picture proportionally.

Scanner calibration

A program that helps adjust the scanner to achieve stable colors and work with a printer. Calibration gives better scanning results and should be run whenever changing printing equipment, toner, and inks, and whenever getting poor results when printing pictures.

Screen calibration

A program that helps adjust the computer screen to get the best display of scanned pictures and documents. This program is run during installation and should be used again any time that the computer screen or the lighting around the computer is changed.

SCSI (Small computer system interface)

Specification of interface to computer equipment like disks, printers, scanners etc.

SCSI card

The printed circuit card that came with the scanner. With its driver software, the card allows the computer to talk to the scanner. The card is ASPI compatible with a SCSI-II output connector.

Shadow detail

The amount of detail contained in the dark parts of an image. It is desirable to maintain shadow detail, but there is a risk of decreasing overall contrast if lightening the shadow too much in an attempt to expose additional detail. If an image is scanned without shadow detail, it will be impossible to regain detail using an image editing program.

Shadow

The darkest part of a picture; reproduced as black onscreen or when printed.

Sharpness

An attribute of a scanned image and also an attribute of scanner quality.

sRGB

Hewlett-Packard and Microsoft proposed the addition of support for a standard color space, sRGB, within the Microsoft operating systems, HP products, the Internet, and all other interested vendors. The aim of this color space is to complement the current color management strategies by enabling a third method of handling color in the operating systems, device drivers and the Internet that utilizes a simple and robust device independent color definition. This is to provide good quality and backward compatibility with minimum transmission and system overhead. Based on a calibrated colorimetric RGB color space well suited to Cathode Ray Tube (CRT) monitors, television, scanners, digital cameras, and printing systems, such a space can be supported with minimum cost to software and hardware vendors.

Stitching

In large format multiple CCD camera scanners, electronic stitching adjusts for overlap in the field of view of adjacent cameras. Automatic stitching at start of scan ensures that each camera captures the correct number of pixels independently of mechanical and thermal changes.

Subtractive colors

The subtractive primary colors: cyan, magenta, yellow. As ink applied to a piece of paper by a printer, these colors absorb light and alter the colors seen by looking at the printed paper. Cyan ink absorbs the red third of the spectrum, magenta ink absorbs the green third, and yellow ink absorbs the blue third. This should theoretically cause the viewer to see a black color, but due to unavoidable impurities in the inks, there is still light reflected and the viewer sees a muddy brown. The absence of CMY pigments results in white.

TIFF – Tagged image file format

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One of the most common graphic file formats for line-art and photographic images.

Tonal distribution

Tonal distribution describes the distribution of various bright or dark tones within an image. During the scanning or image editing stage, tones can be redistributed, lightening a dark image or darkening a light one.

Tone compression

A term used in scanning and image editing that refers to compressing the broad range of tones and colors in an image down to the narrower range available on a printer.

Tone curves

The shape of the tone transfer curves can be adjusted by the user to alter color or tone correction. The lower left end of the curve typically represents the dark portions of a picture and an upward bend will typically lighten the shadows. Similar capabilities exist by working with the middle or highlight parts of the curve. In this way it is possible to alter only certain tonal ranges of an image without making unwanted changes to other parts of the image.

Tone

Any color or neutral that is denser than white.

True color

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True color systems provide eight bits for each of the three components (red, green and blue). Therefore true color is often referred to as 24-bit color.

TWAIN

A standard method of communications that programs can use to send instructions to hardware (such as scanners) and receive data back from them (such as pictures).

UCR – Under color removal

A color separation setting used on color photographs where cyan, magenta and yellow inks are removed from dark, neutral areas and substituted by black ink. The advantages are a reduction in overall ink usage. See also GCR.

Vector drawing (vector file)

Consists of mathematically defined elements, such as “Line from A to B”, “Circle with center and radius”, etc. CAD systems use vector drawings because of their accuracy, relatively low memory requirement and data-file sizes compared to raster images.

Vectorization

Also called raster-to-vector conversion (RTV). The process of automatically converting a raster (bit-mapped) image into a vector (CAD) drawing.

White level

White level is a setting in scan programs used if an original has a background that is not completely white, but ought to be. To get the background to appear as pure white, the white level can be set to a lower value.

White point adjustment

An adjustment that will determine the amount of highlight detail in an image. The white point should be set so that the lightest part of an image will only just have zero detail.

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XYZ

The CIE system is based on the description of color as a brightness (luminance) component Y, and two additional components X and Z. The spectral weighting curves of X and Z have been standardized by CIE, based on statistics from experiments involving human observers. XYZ tri-stimulus values can describe any color.

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Zoom

The ability to enlarge or shrink the view of the picture in a window. Zoom does not alter the size of the final scanned picture; it only provides a better view while creating a selection border on the screen.