Estimation of Perceived Background Tissue Complexity in Mammograms

摘要

Two methods for estimation of location-dependent background tissue complexity (BTC) are proposed. The methods operate by calculating the lowest possible amplitude for which a small superimposed lesion remains visible at a given location in a mammogram: the higher BTC, the larger lesion insertion threshold amplitude. The visibility analysis is based on comparing a region of interest pre- and post-lesion using structural similarity metric (SSIM) in one method. The other proposed estimator is based on just noticeable difference (JND) notion Barten used in modeling contrast sensitivity function (we theorize that lesion detection is equivalent to detection of one cycle of a sinusoid). The proposed BTC estimators are evaluated by comparing them against the lesion insertion amplitude required for visibility set by a human observer. Our results indicate that both estimators correlate with each other (Spearman rank correlation coefficient r_s of 0.76) and outperform constant insertion amplitude in terms of correlation with perceived tissue complexity. The SSIM-based estimator has a higher correlation with the human observer over 24 locales that the estimators disagreed most or both predicted large BTC (r_5 of 0.73, vs. 0.34 for JND-based estimator). The proposed estimators may be used to construct a BTC-aware model observer with applications such as optimization of contrast-enhanced medical imaging systems, and creation of an image dataset to match the characteristics of a given population.