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Related Concept Videos

Chi-square Analysis02:46

Chi-square Analysis

The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
Chi-square Distribution01:10

Chi-square Distribution

How does one determine if bingo numbers are evenly distributed or if some numbers occurred with a greater frequency? Or if the types of movies people preferred were different across different age groups or if a coffee machine dispensed approximately the same amount of coffee each time. These questions can be addressed by conducting a hypothesis test. One distribution that can be used to find answers to such questions is known as the chi-square distribution. The chi-square distribution has...
Finding Critical Values for Chi-Square01:18

Finding Critical Values for Chi-Square

Consider a curve representing sample data drawn randomly from a normally distributed population. One must construct confidence intervals to estimate or to test a claim regarding the population standard deviation. For example, a 95% confidence interval covers 95% of the area under the curve, and the remaining 5% is equally distributed on either side of the curve. To achieve such confidence intervals, one must determine the critical values. The critical values are simply the values separating the...
Test for Homogeneity01:23

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...

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Related Experiment Video

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Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins
11:01

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

Published on: November 17, 2016

Comparison of chi-square and join-count methods for evaluating digital image data.

K S Chuang1, H K Huang

  • 1Dept. of Radiol. Sci., California Univ., Sch. of Med., Los Angeles, CA.

IEEE Transactions on Medical Imaging
|January 1, 1992
PubMed
Summary
This summary is machine-generated.

This study assesses the importance of each pixel bit in radiological images to determine contrast resolution. Statistical tests reveal image data patterns, aiding in the analysis of digital imaging modalities.

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Area of Science:

  • Medical Imaging
  • Digital Image Analysis
  • Radiology

Background:

  • Digital radiological images contain vast amounts of data.
  • Understanding the contribution of each bit to image quality is crucial.
  • Assessing contrast resolution is vital for accurate diagnosis.

Purpose of the Study:

  • To evaluate the significance of individual pixel bits in digital radiological images.
  • To determine the contrast resolution of images from various modalities.
  • To assess the gray-level dynamic range of digital imaging systems.

Main Methods:

  • Utilized join-count statistic to measure spatial coherence among pixels.
  • Employed chi-square goodness of fit test to assess random data distribution.
  • Applied statistical tests on residual images derived from smoothed original images, bit plane by bit plane.

Main Results:

  • Identified the most significant bits contributing to contrast resolution.
  • Demonstrated the effectiveness of statistical methods in analyzing image data.
  • Evaluated images from computerized tomography, magnetic resonance, and computed radiography.

Conclusions:

  • The proposed statistical methods are effective for determining contrast resolution in digital radiological images.
  • Both join-count and chi-square tests are computationally efficient and easy to implement.
  • This approach aids in understanding the gray-level dynamic range and data significance in medical imaging.