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Related Concept Videos

Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

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The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
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Standard Deviation01:10

Standard Deviation

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The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The standard deviation value is small when the data are concentrated close to the mean, exhibiting slight variation or spread. The standard deviation value is never negative, it is either positive or zero. The standard deviation is larger when the data values are more spread out from the mean, which means the data values are exhibiting more variation.
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Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
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Calculating Standard Deviation01:08

Calculating Standard Deviation

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The standard deviation is the most common measure of variation. It is a value that tells us how far a data value is from the mean value in a dataset. Further, the standard deviation is always a positive value or zero.
The standard deviation value is small when all the data is concentrated close to the mean. Here the data exhibits low variation. The standard deviation value is larger when the data values are more spread out from the mean. Here, the data displays high...
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Standard Deviation of Calculated Results01:14

Standard Deviation of Calculated Results

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Standard deviation measures the spread of data around the mean value. Many large data sets follow a Gaussian distribution, also known as a normal distribution. This distribution is bell-shaped curved, with the most frequently observed value (mean or central value) in the middle. The farther away from the central value, the greater the deviation from the central value, and the lower the frequency.
A broad Gaussian distribution curve has a wider standard deviation, representing a data set with...
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Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration
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Fast and robust standard-deviation-based method for bulk motion compensation in phase-based functional OCT.

Xiang Wei, Acner Camino, Shaohua Pi

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    Summary
    This summary is machine-generated.

    A new method accurately compensates for bulk motion in phase-based OCT imaging. This technique improves image quality and computational speed for functional OCT applications like OCT angiography and Doppler OCT.

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    Area of Science:

    • Ophthalmology
    • Biomedical Optics
    • Medical Imaging

    Background:

    • Phase-based optical coherence tomography (OCT), including OCT angiography (OCTA) and Doppler OCT, is susceptible to phase shifts caused by subject bulk motion.
    • Existing bulk motion compensation methods often lack accuracy and cost-effectiveness.

    Purpose of the Study:

    • To introduce a novel and efficient bulk motion compensation method for phase-based functional OCT.
    • To address the limitations of traditional compensation techniques in OCT imaging.

    Main Methods:

    • A new method derives bulk motion-induced phase shifts by solving an equation using the standard deviation of phase-based OCTA and Doppler OCT flow signals.
    • The technique was validated using rodent retinal images from a visible light OCT prototype and human retinal images from a commercial system.

    Main Results:

    • The proposed method significantly enhanced image quality in phase-based functional OCT.
    • Computational speed was notably improved compared to two conventional phase compensation methods.
    • Effective compensation of bulk motion artifacts was demonstrated in both rodent and human retinal imaging.

    Conclusions:

    • The novel method offers a superior solution for bulk motion compensation in phase-based functional OCT.
    • This advancement holds potential for improved diagnostic accuracy and efficiency in OCT-based retinal imaging.