Advantages of fitting contrast curves using logistic function: a technical note

Oral Surg Oral Med Oral Pathol Oral Radiol. 2013 Mar;115(3):e60-3. doi: 10.1016/j.oooo.2012.09.090. Epub 2013 Jan 9.


Objective: The aim of this article is to demonstrate how the contrast properties of an imaging system can be ideally fitted with the use of stripe patterns and the logistic function.

Study design: Stripe patterns with defined amounts of line pairs (lp/mm) per mm (10-20 lp/mm) were recorded with the use of digital photostimulable storage phosphor. Scan data and normalized image data were analyzed with the use of ImageJ and MatLab to calculate different contrast curves.

Results: For original scan data, the goodness of fit was 0.0000019 (sum of squared error [SSE]). The R-square was 0.9998. For normalized data the goodness of fit was 0.0007 (SSE) and the R-square 0.998. An amount of 50% contrast could be calculated to be found on 11.67 lp/mm in normalized images.

Conclusions: This article addresses a potentially new approach to compare digital x-ray modalities using a direct assessment of a known technical target.

MeSH terms

  • Algorithms
  • Humans
  • Image Processing, Computer-Assisted / statistics & numerical data*
  • Logistic Models
  • Radiographic Image Enhancement
  • Radiography, Dental, Digital / instrumentation
  • Radiography, Dental, Digital / statistics & numerical data*
  • X-Ray Intensifying Screens / statistics & numerical data