Shadow Imaging
Shadow Imaging - Related Videos
08:41
Single Wavelength Shadow Imaging of Caenorhabditis elegans Locomotion Including Force Estimates
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Cited by 2 •
2014
The technique presented here measures the path of freely swimming microscopic species using single wavelength exposure. C. elegans are used to demonstrate shadow imaging as an inexpensive alternative to costly microscopes. This technique can be adapted to accommodate various orientations, environments and species to measure direction, speed, acceleration and forces.
13:45
Hybrid µCT-FMT imaging and image analysis
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Cited by 34 •
2015
We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (µCT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.
08:32
Ultrasound Images of the Tongue: A Tutorial for Assessment and Remediation of Speech Sound Errors
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Cited by 41 •
2017
Ultrasound imaging can be used to display the shape and movements of the tongue in real time during speech. The images can be used to determine the nature of speech sound errors. Visual feedback of the tongue can be used to facilitate improvements in speech sound production in clinical populations.
08:19
Lensless On-chip Imaging of Cells Provides a New Tool for High-throughput Cell-Biology and Medical Diagnostics
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Cited by 24 •
2009
Lensfree on-chip imaging and characterization of cells is illustrated. This on-chip cell imaging approach provides a compact and cost-effective tool for medical diagnostics and high-throughput cell biology applications, making it especially suitable for resource poor settings.
09:08
Multiple-mouse Neuroanatomical Magnetic Resonance Imaging
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Cited by 59 •
2011
Magnetic resonance imaging (MRI) has become an increasingly popular tool for examining the phenotype of genetically altered mice. This article illustrates the methods necessary to achieve high-throughput phenotyping of genetically altered mice using multiple-mouse MRI.
FAQs
Image formation depends on how a specimen changes transmitted illumination. Light blocked or scattered by the object produces intensity differences at the detector. Computational reconstruction or image analysis interprets those differences, linking the optical pattern to measurable features such as size, shape, position, and motion. This supports quantitative observation without requiring a fluorescent signal.
Avoiding fluorescent tags reduces sample preparation and photobleaching, which is the loss of fluorescence during illumination. This makes label-free observation useful when researchers need to monitor specimens without adding fluorescent markers or when repeated imaging could weaken a fluorescent signal. The approach therefore supports continued observation of biological samples while preserving a simpler imaging workflow.
Changes in shadow intensity can provide information about a specimen’s size, shape, position, or motion. These measurements allow image analysis to distinguish physical and dynamic features rather than only recording an undifferentiated optical signal. For biological specimens moving in flow, the resulting information can support tracking and population-level analysis.
The recorded information depends on the interaction between transmitted illumination and the specimen, because the object blocks or scatters part of the light reaching the detector. Detector intensity changes then become the basis for computational reconstruction or image analysis. The quality and usefulness of the result depend on how clearly those changes represent object features and movement.
A typical workflow directs transmitted illumination through or toward the biological specimen, records the resulting shadow on a detector, and analyzes changes in light intensity. Computational reconstruction may then convert the recorded pattern into information about the object. This sequence enables measurements of size, shape, position, or motion from the same label-free observation.
Researchers may choose this approach for high-throughput analysis of cells, microorganisms, or particles in flow. It is also useful for tracking dynamic processes, screening populations, and studying specimens that are difficult to label. Because the method reduces sample preparation and photobleaching, it can support biological observations where fluorescent tagging would be inconvenient or limiting.
By capturing information from cells, microorganisms, or particles in flow, the method can support analysis across many specimens rather than focusing only on a single labeled object. Image-derived measurements of size, shape, position, and motion help characterize populations. This makes the technique relevant to screening workflows and high-throughput biological studies.
Motion measurements allow researchers to follow dynamic processes as specimens change position over time. In flow-based biological analysis, tracking can be applied to cells, microorganisms, and particles, while broader image analysis can compare movement with other features such as size or shape. The resulting observations help investigate specimen behavior without relying on fluorescent labels.