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Non-Gaussian statistical properties of virtual breast phantoms

2014; SPIE; Volume: 9037; Linguagem: Inglês

10.1117/12.2044446

1996-756X

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

AI in cancer detection

Images derived from a "phantom" are useful for characterizing the performance of imaging systems. In particular, the modulation transfer properties of imaging detectors are traditionally assessed by physical phantoms consisting of an edge. More recently researchers have come to realize that quantifying the effects of object variability can also be accomplished with phantoms in modalities such as breast imaging where anatomical structure may be the principal limitation in performance. This has driven development of virtual phantoms that can be used in simulation environments. In breast imaging, several such phantoms have been proposed. In this work, we analyze non-Gaussian statistical properties of virtual phantoms, and compare them to similar statistics from a database of breast images. The virtual phantoms assessed consist of three classes. The first is known as clustered-blob lumpy backgrounds. The second class is "binarized" textures which typically apply some sort of threshold to a stochastic 3D texture intended to represent the distribution of adipose and glandular tissue in the breast. The third approach comes from efforts at the University of Pennsylvania to directly simulate the 3D anatomy of the breast. We use Laplacian fractional entropy (LFE) as a measure of the non-Gaussian statistical properties of each simulation. Our results show that the simulation approaches differ considerably in LFE with very low scores for the clustered-blob lumpy background to very high values for the UPenn phantom. These results suggest that LFE may have value in developing and tuning virtual phantom simulation procedures.