Matching statistical object models to real images

Matthew A. Kupinski, Eric Clarkson, Harrison H. Barrett

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


We advocate a task-based approach to measuring and optimizing image quality; that is, optimize imaging systems based on the performance of a particular observer performing a specific task. This type of analysis can require numerous images and is, thus, infeasible with real patients. Researchers are forced to employ statistical models from which they can produce as many images as required. We have developed methods to accurately fit statistical models of continuous objects to real images. The fitted models can be used for hardware optimizations as well as image-processing optimizations. We have employed a continuous lumpy object model in this research and found that our method can accurately determine model parameters in simulation.

Original languageEnglish (US)
Pages (from-to)37-42
Number of pages6
JournalProceedings of SPIE-The International Society for Optical Engineering
StatePublished - 2002


  • Hardware optimization
  • Image quality
  • Object statistics

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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