MTF correction for optimizing softcopy display of digital mammograms: Use of a vision model for predicting observer performance

Elizabeth A. Krupinski, Hans Roehrig, Michael Engstrom, Jeffrey Johnson, Jeffrey Lubin

Research output: Contribution to journalConference article

6 Scopus citations

Abstract

The goal of this project was to develop an efficient method of optimizing CRT monitor performance for digital mammography. In this study we examined the effects on performance of processing images to compensate for limitations in the MTF of the CRT monitor. The Sarnoff JNDmetrix vision model is based on just-noticeable difference measurement and frequency-channel vision-modeling principles. Given two images as input the model returns accurate, robust estimates of their discriminability. Model predictions are then compared with human performance. Mammographic images (n = 250) with microcalcifications were viewed by six radiologists, The images were viewed once in original unprocessed form and once after processing. Results were compared with output of the model that was used to predict differences in perceptibility of calcifications using luminance data measured with a high-resolution CCD camera. Human performance was better with the MTF compensated images at all contrast levels. The JNDmetrix model predicted the same pattern of results. Correlation between human and model observer performance was very high. Using image processing methods to compensate for limitations in the MTF of CRT monitors can improve the detection performance of radiologists searching for microcalcifications.

Original languageEnglish (US)
Pages (from-to)323-327
Number of pages5
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5034
DOIs
StatePublished - Sep 12 2003
EventMedical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment - San Diego, CA, United States
Duration: Feb 18 2003Feb 20 2003

Keywords

  • CRT monitor
  • MTF
  • Observer performance
  • Vision model

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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