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Related Experiment Video

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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

Quantification of image quality using information theory.

Takanaga Niimi1, Hisatoshi Maeda, Mitsuru Ikeda

  • 1Department of Radiological Technology, Nagoya University School of Health Sciences, 1-1-20 Daiko-minami, Higashi-ku, Nagoya 461-8673, Japan. takanaga213@yahoo.co.jp

Australasian Physical & Engineering Sciences in Medicine
|November 16, 2011
PubMed
Summary
This summary is machine-generated.

Information theory, using Shannon

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Area of Science:

  • Medical Imaging
  • Information Theory
  • Radiography

Background:

  • Assessing medical image quality is crucial for accurate diagnosis.
  • Traditional methods may not fully capture perceptual aspects of image quality.
  • Information theory offers a quantitative approach to image analysis.

Purpose of the Study:

  • To evaluate the utility of information theory for visual assessment of image quality.
  • To apply Shannon's information theory to quantify information losses (IL) in mammography images.
  • To correlate information loss metrics with established image quality figures.

Main Methods:

  • Computed radiography images of a contrast-detail mammography (CDMAM) phantom were acquired at varying radiation doses.
  • Shannon's information theory was used to calculate information content (entropy) based on detection probabilities.
  • Information losses (IL) were computed as the difference between information content and obtained information.
  • Total information losses (TIL) were calculated and correlated with image quality metrics.

Main Results:

  • Information losses (IL) decreased significantly with increasing disk diameters and radiation doses.
  • Total information losses (TIL) showed a strong correlation (r = 0.985) with image quality figures.
  • TIL demonstrated sensitivity to the distribution of individual observer image reading abilities, even with consistent average reading ratios.

Conclusions:

  • Information theory, specifically Shannon's approximation, provides a valuable tool for assessing visual image quality in mammography.
  • Total information losses (TIL) effectively correlate with image quality figures and are sensitive to observer variability.
  • This approach is expected to enhance the evaluation of medical image quality by accounting for perceptual factors.