Background Subtraction
Background Subtraction - Related Videos
06:03
AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells
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Cited by 2 •
2023
Current methods for analyzing the intracellular dynamics of polarized single cells are often manual and lack standardization. This manuscript introduces a novel image analysis pipeline for automating midline extraction of single polarized cells and quantifying spatiotemporal behavior from time lapses in a user-friendly online interface.
01:18
Thermodynamic Background
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2026
The law of mass action states that "the rate of a chemical reaction is directly proportional to the product of the molar concentrations of the reactants." It means that the more 'active mass' or 'concentration' of the reactants present, the faster the reaction will proceed.In a chemical reaction, there are forward and reverse reactions. The forward reaction is the process where the reactants combine to form products. The reverse reaction is the process where the products break down to form the...
15:04
Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy
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Cited by 5 •
2011
We discuss the construction and operation of a complex nonlinear optical system that uses ultrafast all-optical switching to isolate Raman from fluorescence signals. Using this system we are able to successfully separate Raman and fluorescence signals utilizing pulse energies and average powers that remain biologically safe.
01:22
Detection of Internal Carotid Artery Narrowing Using Digital Subtraction Angiography
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2025
Position a human patient with internal carotid stenosis, a condition characterized by narrowing of the internal carotid artery, on the imaging table for digital subtraction angiography (DSA). Insert an angiocatheter into the common carotid artery at the fourth cervical vertebra level. Capture a pre-contrast X-ray image to record background structures like bones and soft tissues using X-ray irradiation. Administer an iodine-based contrast medium into the carotid artery. The contrast medium...
02:27
Background and Environment Affect Phenotype
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2021
Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype. An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
FAQs
The choice depends on how the scene is represented over time and on how stable the surroundings remain. Reference images provide a comparison baseline, whereas temporal pixel statistics estimate background behavior from successive frames. In either case, the model must represent relatively static content well enough that subtraction emphasizes biological objects rather than ordinary scene structure.
Illumination changes can create differences that resemble foreground, while image noise can introduce spurious pixel-level differences. Gradual background motion presents a similar challenge because the estimated surroundings no longer match each new frame. Accounting for these effects improves detection reliability, reducing the chance that thresholding will produce misleading foreground masks.
Subtraction produces differences, but those differences are not automatically a usable object outline. Thresholding converts the difference image into a foreground mask by selecting changes that are sufficiently distinct from the modeled background. That mask can then support segmentation, motion tracking, and quantitative measurements, linking pixel-level processing to interpretable biological observations.
Relatively static surroundings give the model a consistent estimate of what should be removed. When the background changes gradually, subtraction can confuse that motion with a biological object or leave unwanted structure in the result. This is why background stability matters for reliable masks and for measurements derived from those masks in microscopy or time-lapse recordings.
A basic workflow starts with images or video containing the biological scene, followed by selection of reference information or temporal pixel behavior for modeling. Each new frame is compared with that estimate, and the resulting differences are thresholded to create a foreground mask. The mask can then be used for segmentation, tracking, or measurement.
The method is suited to scenes in which cells, organisms, particles, or tissue features appear against surroundings that are comparatively stable. Microscopy images and time-lapse recordings are especially relevant because objects can be isolated across frames for further analysis. This supports bioengineering studies that require biological structures or moving entities to be distinguished from complex image backgrounds.
Foreground masks identify image regions that differ from the estimated surroundings, providing a basis for locating biological features and following their changes across recordings. Researchers can use these regions for segmentation, motion tracking, and quantitative measurements. The resulting data convert complex microscopy or time-lapse images into more interpretable information about cells, organisms, particles, or tissue features.