Grayscale Imaging
Grayscale Imaging - Related Videos
06:50
Enabling High Grayscale Resolution Displays and Accurate Response Time Measurements on Conventional Computers
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Cited by 9 •
2012
Conventional computer hardware can not generate visual stimuli with sufficiently high grayscale resolution and measure response times with sufficient accuracy. We describe how to use the VideoSwitcher to produce high-resolution monochromatic displays, and the RTbox to measure response times with high accuracy on conventional computer hardware.
06:41
A New Technique for Quantitative Analysis of Hair Loss in Mice Using Grayscale Analysis
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Cited by 3 •
2015
Alopecia is a common form of hair loss which can occur in many different conditions, including as a side-effect of chemotherapy. We have developed a method to quantify hair loss in mice, utilizing a standard gel imager to perform a grayscale analysis, to facilitate study of promising new alopecia therapies.
13:10
Combined Near-infrared Fluorescent Imaging and Micro-computed Tomography for Directly Visualizing Cerebral Thromboemboli
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Cited by 8 •
2016
This protocol describes the application of combined near-infrared fluorescent (NIRF) imaging and micro-computed tomography (microCT) for visualizing cerebral thromboemboli. This technique allows the quantification of thrombus burden and evolution. The NIRF imaging technique visualizes fluorescently labeled thrombus in excised brain, while the microCT technique visualizes thrombus inside living animals using gold-nanoparticles.
08:05
Doppler Ultrasonography for Live Imaging and Quantification of Ovarian Vascular Function in Mice
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Cited by 1 •
2025
Changes in ovarian blood flow during the preovulatory period are essential for ovulation. Doppler ultrasonography has been used in humans and large animals to assess ovarian hemodynamics and perfusion. A protocol is presented for applying Doppler ultrasonography to evaluate ovarian hemodynamics during the preovulatory and periovulatory phases in mice.
07:57
Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving (SSLE) of Glass Crystals
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2017
A methodology is described herein for representing anatomical imaging data within crystals. We create scaled three-dimensional models of biomedical imaging data for use in Sub-Surface Laser Engraving (SSLE) of crystal glass. This tool offers a useful complement to computational display or three-dimensionally printed models used within clinical or educational settings.
FAQs
Each pixel receives an intensity value after detected light or another imaging signal is converted into numerical data. The resulting array preserves where the signal occurs and how strongly it appears at each location. This pixel-level representation allows researchers to move from visual inspection toward measurements of signal distribution, abundance, and structural features in genetic samples.
Brightness differences can indicate variation in signal abundance, spatial distribution, or underlying structure. For example, stronger and weaker regions may distinguish bands in a sample or separate areas with different cellular or molecular features. Interpreting intensity alongside location helps researchers determine whether an observed pattern reflects where a signal occurs, how much is present, or both.
Pixel-level intensity values provide the numerical basis for separating regions within an image, a process called image segmentation. Segmentation can isolate bands, cellular areas, or other signal-containing features from surrounding regions. Once these areas are distinguished, researchers can analyze them more consistently and quantify patterns related to DNA fragments, gene expression, or cellular phenotypes.
In chromosome preparations, grayscale data can make differences in brightness and spatial structure available for band detection and image segmentation. These measurements help distinguish chromosome features within the preparation and support more consistent visual analysis. Rather than relying only on appearance, researchers can use the recorded intensity pattern to assess genetic structures with greater numerical precision.
Electrophoresis gels and autoradiographic assays produce spatial signal patterns that can be represented as varying intensities. Researchers can examine the location and strength of bands to assess DNA fragments or other genetic signals. Recording those differences numerically supports band detection and signal quantification, making comparisons among visible regions more consistent than unaided visual inspection.
When fluorescence microscopy signals are represented by intensity values, the resulting images can support analysis of gene expression patterns, cellular phenotypes, and spatial signal distribution. Researchers can examine where signals occur and compare their measured brightness across image regions. This provides a numerical complement to visual interpretation when studying genetic features in cells or other biological preparations.