Related Experiment Video
Updated: Jul 10, 2026

Test Samples for Optimizing STORM Super-Resolution Microscopy
Published on: September 6, 2013
Analysis of streak artefacts on CT images using statistics of extremes
1Department of Radiological Technology, Nagoya University School of Health Sciences, 1-20 Daikominami 1-chome, Higashi-ku, Nagoya 461-8673, Japan. imai@met.nagoya-u.ac.jp
Abstract:
We have analysed the statistical characteristics of streak artefacts on CT images using the statistics of extremes, and have devised a new method of evaluating streak artefacts on CT images. The CT images of four polymer tubes placed on the chest wall of a commercially available chest phantom were used as the target objects for our analysis. 40 parallel line segments with a length of 20 pixels were placed perpendicular to numerous streak artefacts on the polymer tube image, and the largest difference between adjacent CT values in each of the 40 CT value profiles of these line-segments was employed as a feature variable of a streak artefact; these feature variables have been analysed by extreme value theory. Using the mean rank method, a Gumbel distribution was shown to be the most suitable extreme value distribution for the largest difference between adjacent CT values in each CT value profile. This enabled us to demonstrate that the streak artefacts on CT images can be statistically modelled by a Gumbel distribution. Both the location parameter and the scale parameter of the estimated Gumbel probability density distribution were large on the CT slices in which the shoulder bone or liver was included.
More Related Videos
09:09Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data
Published on: December 17, 2015
07:05Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
Published on: June 18, 2021
Related Concept Videos
Outliers and Influential Points
What Are Outliers?
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
Quantifying and Rejecting Outliers: The Grubbs Test
Detection of Gross Error: The Q Test
Microsoft Excel: Finding Central Tendency, Skew, and Kurtosis
Mean: The arithmetic average of all data points. It is calculated by adding all the values together and dividing by the number of values. The mean is sensitive to extreme values (outliers).
Median: The middle value when the data points are arranged in ascending or descending...
Unusual Results
According to the range rule of thumb, any value above or below two standard deviations, 2σ from the mean, μ is considered unusual.
Maximum unusual value = μ + 2σ
Minimum unusual value...