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Related Concept Videos

Probability Histograms01:17

Probability Histograms

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.
Histogram01:05

Histogram

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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Updated: May 7, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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Published on: August 30, 2013

CT texture analysis using the filtration-histogram method: what do the measurements mean?

Kenneth A Miles1, Balaji Ganeshan, Michael P Hayball

  • 1Institute of Nuclear Medicine, University College London, UK; Centre for Molecular Imaging, Princess Alexandra Hospital, Brisbane, Australia.

Cancer Imaging : the Official Publication of the International Cancer Imaging Society
|September 25, 2013
PubMed
Summary

Computed tomography (CT) texture analysis aids cancer prognosis. This study explains how image features relate to CT texture parameters, improving interpretation of cancer imaging in oncology.

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging Analysis

Background:

  • Texture analysis in computed tomography (CT) is a developing method for cancer prognosis and treatment response assessment.
  • Understanding the link between CT image features and quantitative texture parameters is crucial for clinical application.

Purpose of the Study:

  • To illustrate the correlation between image and histological features with CT texture parameters.
  • To explain the relationships between specific image features and texture parameters using computer modeling.
  • To aid the interpretation of CT texture analysis in oncology.

Main Methods:

  • Utilizing a filtration-histogram approach for CT texture analysis.
  • Applying computer modeling to generate texture parameters from hypothetical images.
  • Analyzing texture in clinical CT scans of human tumors.

Main Results:

  • CT texture parameters are related to the number, brightness/contrast, and variability of highlighted image features.
  • Computer modeling effectively explains the relationships between image features and texture parameters.
  • These relationships are observable in both simulated and clinical CT data.

Conclusions:

  • The filtration-histogram method provides quantifiable texture parameters linked to specific image features.
  • Understanding these feature-parameter relationships enhances the interpretation of CT texture analysis in cancer patients.
  • This knowledge supports the clinical utility of CT texture analysis for prognosis and treatment monitoring in oncology.