Contrast sensitivity of digital imaging display systems: contrast threshold dependency on object type and implications for monitor quality assurance and quality control in PACS

Med Phys. 2009 Aug;36(8):3682-92. doi: 10.1118/1.3173816.

Abstract

The American Association of Physicists in Medicine Task Group 18 has published standards and quality control (QC) guidelines to ensure consistency and optimal quality for digital image display systems (DIDSs). In many of these recommended QC tests, static test patterns that contain low-contrast objects are often used to assess and validate the quality of a DIDS. These low-contrast objects often have the shape of circular disks or squares with sharp edges, neither of which resemble most of the diagnostic findings in medical images. On the other hand, circular objects with fuzzy boundaries bear a closer resemblance to lung nodules in chest radiography and masses in mammography; thus, they may be more clinically relevant in assessing display system quality. In this article human observers' contrast sensitivities of circular objects with sharp edges and those with fuzzy ones were investigated. The contrast thresholds of human viewers using a consumer-grade color LCD monitor and a medical-grade monochrome LCD monitor were measured for objects of various sizes displayed against uniform backgrounds with various luminance levels. Contrast-detail curves for circular objects with sharp edges and those with fuzzy boundaries were measured and compared. It was found that contrast thresholds for objects with fuzzy boundaries were higher (i.e., the objects were more difficult to detect) than those with sharp edges. Objects with fuzzy boundaries were potentially more sensitive in distinguishing quality differences among image display devices and thus may be a better QC measurement in detecting subtle deterioration in image display devices.

MeSH terms

  • Adult
  • Color
  • Diagnostic Imaging / methods*
  • Diagnostic Imaging / statistics & numerical data
  • Humans
  • Observer Variation
  • Quality Control
  • Sensitivity and Specificity