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Updated: Jul 10, 2026

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ
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Automatic image quality quantification and mapping with an edge-preserving mask-filtering algorithm.

M Kortesniemi1, Y Schenkel, E Salli

  • 1HUS Helsinki Medical Imaging Center, Helsinki University Central Hospital, Helsinki, Finland. mika.kortesniemi@hus.fi

Acta Radiologica (Stockholm, Sweden : 1987)
|October 27, 2007
PubMed
Summary

An automatic method was developed for assessing digital radiology image quality. This image quality score (IQs) efficiently quantifies image quality and can be visualized on a 2D map for further analysis.

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Area of Science:

  • Medical Imaging
  • Radiology
  • Image Processing

Background:

  • Digital radiology generates vast datasets requiring validated image quality.
  • Diagnostic accuracy relies on consistent and reliable image quality assessment.
  • Current methods may not be efficient for large-scale data analysis.

Purpose of the Study:

  • To develop an automated method for quantifying image quality in digital radiology.
  • To create a reliable and efficient tool for image quality assessment.
  • To enable objective validation of diagnostic imaging operations.

Main Methods:

  • A filtering algorithm with a moving square mask was employed to generate local intensity and noise maps.
  • Image quality scores (IQs) were computed from filtered image data.

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  • The method was validated using technical and anthropomorphic phantoms with varied radiation dose, field of view (FOV), and image content, as well as a clinical CT brain image.

    • Main Results:

    • Image quality scores (IQs) positively correlated with radiation dose (CTDIvol), increasing from 0.51 to 0.82 as dose rose from 9.2 to 74.3 mGy.
    • High correlation (R²=0.99 and R²=0.98) was observed between IQs and pixel noise across varied conditions.
    • Automatic tube current modulation improved image quality score consistency by approximately 60% compared to fixed tube current.

    Conclusions:

    • The developed image quality score (IQs) offers an efficient automated tool for image quality quantification.
    • The method generates a 2D image quality map, facilitating detailed image analysis.
    • This automated approach aids in validating diagnostic operations in digital radiology.