Evaluation of non-Gaussian statistical properties in virtual breast phantoms

Craig K. Abbey, Predrag R. Bakic, David D. Pokrajac, Andrew D.A. Maidment, Miguel P. Eckstein, John M Boone

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Images derived from a "virtual phantom" can be useful in characterizing the performance of imaging systems. This has driven the development of virtual breast phantoms implemented in simulation environments. In breast imaging, several such phantoms have been proposed. We analyze the non-Gaussian statistical properties from three classes of virtual breast phantoms and compare them to similar statistics from a database of breast images. These include clustered-blob lumpy backgrounds (CBLBs), truncated binary textures, and the UPenn virtual breast phantoms. We use Laplacian fractional entropy (LFE) as a measure of the non-Gaussian statistical properties of each simulation procedure. Our results show that, despite similar power spectra, the simulation approaches differ considerably in LFE with very low scores for the CBLB to high values for the UPenn phantom at certain frequencies. These results suggest that LFE may have value in developing and tuning virtual phantom simulation procedures.

Original languageEnglish (US)
Article number025502
JournalJournal of Medical Imaging
Issue number2
StatePublished - Apr 1 2019


  • breast phantoms
  • image statistics
  • Laplacian fractional entropy
  • natural scene statistics

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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