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Evaluation of non-Gaussian statistical properties in virtual breast phantoms

Abbey, Craig K. ; Bakic, Predrag R. LU ; Pokrajac, David D. ; Maidment, Andrew D.A. ; Eckstein, Miguel P. and Boone, John M. (2019) In Journal of Medical Imaging 6(2).
Abstract

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... (More)

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.

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author
; ; ; ; and
publishing date
type
Contribution to journal
publication status
published
keywords
breast phantoms, image statistics, Laplacian fractional entropy, natural scene statistics
in
Journal of Medical Imaging
volume
6
issue
2
article number
025502
publisher
SPIE
external identifiers
  • scopus:85069439397
  • pmid:31259201
ISSN
2329-4302
DOI
10.1117/1.JMI.6.2.025502
language
English
LU publication?
no
id
74e50c57-0c0d-4622-923e-6590bb1bc472
date added to LUP
2020-11-07 12:55:30
date last changed
2024-06-14 02:58:56
@article{74e50c57-0c0d-4622-923e-6590bb1bc472,
  abstract     = {{<p>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.</p>}},
  author       = {{Abbey, Craig K. and Bakic, Predrag R. and Pokrajac, David D. and Maidment, Andrew D.A. and Eckstein, Miguel P. and Boone, John M.}},
  issn         = {{2329-4302}},
  keywords     = {{breast phantoms; image statistics; Laplacian fractional entropy; natural scene statistics}},
  language     = {{eng}},
  month        = {{04}},
  number       = {{2}},
  publisher    = {{SPIE}},
  series       = {{Journal of Medical Imaging}},
  title        = {{Evaluation of non-Gaussian statistical properties in virtual breast phantoms}},
  url          = {{http://dx.doi.org/10.1117/1.JMI.6.2.025502}},
  doi          = {{10.1117/1.JMI.6.2.025502}},
  volume       = {{6}},
  year         = {{2019}},
}