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Related Concept Videos

Boxplot01:12

Boxplot

10.8K
Box plots (also called box-and-whisker plots or box-whisker plots) give an excellent graphical image of the concentration of the data. They also show how far the extreme values are from most data. A box plot is constructed from five values: the minimum value, the first quartile, the median, the third quartile, and the maximum value. We use these values to compare how close other data values are to them. To construct a box plot, use a horizontal or vertical number line and a rectangular box. The...
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Modified Boxplots00:57

Modified Boxplots

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A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
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Probability Histograms01:17

Probability Histograms

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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Histogram01:05

Histogram

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The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
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5-Number Summary01:04

5-Number Summary

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In a dataset, the 5-number summary includes the minimum data value, the data value of the first quartile, the median data value or data value of the second quartile, the data value of the third quartile, and the maximum data value. These 5 data values can be visualized as a box and whisker plot.
In a box plot, the minimum and maximum data values represent the lower and upper whiskers in the graph, and the median is designated as the center of the box in the chart. The first quartile and third...
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Microsoft Excel: Median, Quartile range, and Box Plots01:29

Microsoft Excel: Median, Quartile range, and Box Plots

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In Microsoft Excel, calculating the median, interquartile range, and creating box plots can help understand the distribution of your data.
Median and Quartile Range: The median is calculated using the formula `=MEDIAN(range)', which provides the middle value of your data set. Quartiles divide your data into four equal parts. To find the first and third quartiles, use ‘=QUARTILE(range, 1)' and ‘=QUARTILE(range, 3)', respectively. The interquartile range (IQR), which...
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Weighted functional boxplot with application to statistical atlas construction.

Yi Hong1, Brad Davis2, J S Marron1

  • 1University of North Carolina (UNC) at Chapel Hill, NC, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|February 8, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel statistical method for medical image analysis, using weighted functional boxplots to characterize population data. This approach enhances atlas-building for pediatric airways and corpora callosa, aiding in growth pattern analysis and surgical outcome assessment.

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Area of Science:

  • Medical Imaging
  • Statistical Analysis
  • Computational Anatomy

Background:

  • Traditional atlas-building in medical imaging focuses on mean/median representations of population data.
  • Existing methods often overlook detailed statistical characterization of shape and image variations within a population.
  • There is a need for advanced statistical tools to analyze complex data like functions, shapes, and images.

Purpose of the Study:

  • To introduce and propose the weighted functional boxplot for statistical characterization of population data in medical imaging.
  • To enable spatio-temporal atlas-building using kernel regression for functional, shape, or image data.
  • To demonstrate the utility of this method for pediatric upper airway and corpus callosum growth pattern analysis.

Main Methods:

  • Development and application of the weighted functional boxplot.
  • Generalization of statistical concepts (median, percentiles, outliers) to functional, shape, and image data spaces.
  • Utilizing kernel regression for spatio-temporal atlas construction.

Main Results:

  • Successful construction of statistical atlases for pediatric upper airways and corpora callosa.
  • Revealed insights into the growth patterns of these anatomical structures.
  • Demonstrated the application of the developed atlas for assessing the impact of pediatric airway surgery.

Conclusions:

  • The weighted functional boxplot is a powerful tool for statistical characterization in atlas-building.
  • This method provides a robust framework for analyzing complex medical image data and understanding anatomical variations.
  • The approach has significant implications for pediatric growth studies and evaluating surgical interventions.