Jannick P. Rolland,Alexei A. Goon CREOL/Univ. of Central Florida (United States) Eric Clarkson Optical Sciences Ctr./Univ. of Arizona (United States) Liyun Yu CREOL/Univ. of Central Florida (United States)
Image quality assessment in medical imaging requires realistic textured background that can be statistically characterized for the computation of model observers' performance. We present a modeling framework for the synthesis of texture as well as a statistical analysis of both sample and synthesized textures. The model employs a two-component image-decomposition consisting of a slowly, spatially varying mean-background and a residual texture image. Each component is synthesized independently. The technique is demonstrated using radiological breast tissue. For statistical characterization, we compute the two-point probability density functions for the real and synthesized breast tissue textures in order to provide a complete characterization and comparison of their second-order statistics. Similar computations for other textures yield further insight into the statistical properties of these types of random fields.