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Modelling of Subjective Radiological Assessments with Objective Image Quality Measures of Brain and Body CT Images

  • Ilona A. Kowalik-UrbaniakEmail author
  • Jane Castelli
  • Nasim Hemmati
  • David Koff
  • Nadine Smolarski-Koff
  • Edward R. Vrscay
  • Jiheng Wang
  • Zhou Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9164)

Abstract

In this work we determine how well the common objective image quality measures (Mean Squared Error (MSE), local MSE, Signal-to-Noise Ratio (SNR), Structural Similarity Index (SSIM), Visual Signal-to-Noise Ratio (VSNR) and Visual Information Fidelity (VIF)) predict subjective radiologists’ assessments for brain and body computed tomography (CT) images.

A subjective experiment was designed where radiologists were asked to rate the quality of compressed medical images in a setting similar to clinical. We propose a modified Receiver Operating Characteristic (ROC) analysis method for comparison of the image quality measures where the “ground truth” is considered to be given by subjective scores. The best performance was achieved by the SSIM index and VIF for brain and body CT images. The worst results were observed for VSNR.

We have utilized a logistic curve model which can be used to predict the subjective assessments with an objective criteria. This is a practical tool that can be used to determine the quality of medical images.

Keywords

Mean Square Error Image Quality Assessment Body Compute Tomography Quality Assessment Method Objective Image Quality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We thank Prof. Paul Marriott, Department of Statistics and Actuarial Sciences, University of Waterloo for valuable advice with regard to the statistical design of our experiments. This research was supported in part by Discovery Grants from the Natural Sciences and Engineering Research Council of Canada (ERV and ZW).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ilona A. Kowalik-Urbaniak
    • 1
    Email author
  • Jane Castelli
    • 3
  • Nasim Hemmati
    • 4
  • David Koff
    • 3
  • Nadine Smolarski-Koff
    • 3
  • Edward R. Vrscay
    • 1
  • Jiheng Wang
    • 2
  • Zhou Wang
    • 2
  1. 1.Department of Applied Mathematics, Faculty of MathematicsUniversity of WaterlooWaterlooCanada
  2. 2.Department of Electrical and Computer EngineeringFaculty of Engineering, University of WaterlooWaterlooCanada
  3. 3.Department of RadiologyMcMaster UniversityHamiltonCanada
  4. 4.Department of Diagnostic Imaging, Hamilton Health SciencesMcMaster UniversityHamiltonCanada

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