Evaluation of non-Gaussian statistical properties in virtual breast phantoms
(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
- Abbey, Craig K. ; Bakic, Predrag R. LU ; Pokrajac, David D. ; Maidment, Andrew D.A. ; Eckstein, Miguel P. and Boone, John M.
- publishing date
- 2019-04-01
- 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
-
- pmid:31259201
- scopus:85069439397
- 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
- 2024-09-05 08:21:14
@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}}, }