Evaluation of non-Gaussian statistical properties in virtual breast phantoms.

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

doi:10.1117/1.jmi.6.2.025502

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 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.

Highlights

Virtual breast phantoms have many appealing qualities for evaluating breast imaging technology
The main results of this work are power spectra and Laplacian fractional entropy (LFE) plots averaged over orientations and plotted as a function of spatial frequency
The largest divergence between fitted model and data occurs for the OpEx99 model of the clustered-blob lumpy backgrounds (CBLBs) [Fig. 6(a)] at low spatial frequencies (

Summary

Introduction

Virtual breast phantoms have many appealing qualities for evaluating breast imaging technology. In order to be effective, a virtual phantom must accurately capture the effects of patient-structured images. We refer to this objective as phantom realism, with the assumption that a more realistic phantom will more fully capture anatomical effects than a less realistic phantom. It is not clear at this point in time how to validate such a comparison or how such a validation might depend on the task that drives development of the imaging system

Objectives

Methods

Results

Conclusion