Digital Imaging
Digital Imaging - Related Videos
08:45
Shrinkage of Dental Composite in Simulated Cavity Measured with Digital Image Correlation
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Cited by 12 •
2014
In order to understand the spatial development of polymerization shrinkage stress in dental resin-composite restorations, Digital Image Correlation was used to provide full-field displacement/strain measurement of restored model glass cavities by correlating images of the restoration taken before and after polymerization.
09:29
Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens
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Cited by 19 •
2016
We describe the use of digital image correlation to characterize the local surface strain field on vascular tissue samples subjected to uniaxial tensile testing. These measurements facilitate precise quantification of the sample mechanical response and the generation of constitutive stress-strain relations.
11:34
High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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Cited by 9 •
2013
This video describes the fundamentals of digital fringe projection techniques, which provide dense 3D measurements of dynamically changing surfaces. It also demonstrates the design and operation of a high-speed binary defocusing system based on these techniques.
07:42
Imaging of Estrogen Receptor-α in Rat Pial Arterioles using a Digital Immunofluorescent Microscope
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2011
The goal of this article is to demonstrate a method to optimize immunofluorescent detection of estrogen receptor-α (ERα) in rat pial arterial slices using a digital immunofluorescent microscope.
07:38
Multimodal Quantitative Phase Imaging with Digital Holographic Microscopy Accurately Assesses Intestinal Inflammation and Epithelial Wound Healing
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Cited by 18 •
2016
Accurate assessment of anti-inflammatory effects is of utmost importance for the evaluation of potential new drugs for the treatment of inflammatory bowel disease. Digital holographic microscopy provides assessment of inflammation in murine and human colonic tissue samples as well as automated multimodal evaluation of epithelial wound healing in vitro.
FAQs
The detector determines which physical signal becomes image data. Transmitted X-rays, magnetic resonance responses, and reflected ultrasound each provide different forms of visual information before conversion into pixels or three-dimensional voxels. This distinction matters because the resulting data can be processed and interpreted according to the signal measured, supporting different forms of medical visualization and analysis.
Pixels represent image information in two dimensions, whereas voxels organize information within three-dimensional image volumes. This numerical structure allows software to reconstruct, enhance, and quantify visual findings rather than limiting interpretation to a fixed display. As a result, clinicians and researchers can examine medical information spatially and apply computational analysis to support diagnosis or treatment planning.
Computational tools can enhance images, reconstruct visual information, and quantify measurable features within the data. Artificial intelligence adds capabilities such as segmentation, which separates or outlines structures, and pattern recognition, which identifies recurring visual characteristics. These functions can assist clinical decision-making while also supporting more systematic analysis in medical research.
Standardized data formats help images remain usable across electronic records, remote consultation, and research environments. Consistent digital organization makes it easier to share visual information and compare examinations during monitoring of treatment response. This connectivity extends the value of an image beyond its original acquisition, linking visualization with longitudinal assessment and collaborative medical evaluation.
A medical imaging workflow begins when a detector measures a signal, followed by conversion of that measurement into numerical image data. Software may then reconstruct, enhance, or quantify the data before storage and display. The resulting images can enter electronic records or be shared for consultation, allowing visual information to support subsequent interpretation and care decisions.
Its applications include disease detection, diagnosis, treatment planning, surgical procedures, and image-guided procedures. Clinicians can also use imaging to monitor a patient's response over time. Because the information is numerical and can be processed computationally, the same examination may support immediate clinical interpretation as well as later review, measurement, or planning.
Digital storage and standardized formats make visual information easier to share through electronic records and remote consultation. The same data can also support research by enabling computational analysis, reconstruction, enhancement, and quantification. These capabilities connect clinical imaging with broader investigations of disease, treatment response, and patterns that may be examined across medical datasets.
Artificial intelligence can assist with segmentation, pattern recognition, and clinical decision-making. Segmentation helps identify or delineate relevant structures, while pattern recognition examines image features that may inform interpretation. These tools do not replace the imaging data itself; they operate on digitally represented information to support analysis, improve workflow, and contribute to research and clinical assessment.