On anthropomorphic decision making in a model observer

Abstract

By analyzing human readers' performance in detecting small round lesions in simulated digital breast tomosynthesis background in a location known exactly scenario, we have developed a model observer that is a better predictor of human performance with different levels of background complexity (i.e., anatomical and quantum noise). Our analysis indicates that human observers perform a lesion detection task by combining a number of sub-decisions, each an indicator of the presence of a lesion in the image stack. This is in contrast to a channelized Hotelling observer, where the detection task is conducted holistically by thresholding a single decision variable, made from an optimally weighted linear combination of channels. However, it seems that the sub-par performance of human readers compared to the CHO cannot be fully explained by their reliance on sub-decisions, or perhaps we do not consider a sufficient number of subdecisions. To bridge the gap between the performances of human readers and the model observer based upon subdecisions, we use an additive noise model, the power of which is modulated with the level of background complexity. The proposed model observer better predicts the fast drop in human detection performance with background complexity.

Location- and lesion-dependent estimation of mammographic background tissue complexity

“…Estimator LBA is not far from an anthropomorphic model observer, 12 if the area within the immediate surround mask is brighter than BTC (i.e., maximum luminance with the immediate surround mask) by a certain margin and satisfies some other criteria (e.g., small gradient), it may be announced as a lesion. For a known lesion (SKE), this method may be used as the basis of an anthropomorphic search mechanism.…”

“…Note that this method is unsupervised and local (i.e., provides local complexity information) and is shown to perform worse than the supervised BTC estimator in Ref. 12. Recently, Alonzo-Proulx et al 15 and Mainprize et al 16 improved this method by using a calibration-free volumetric breast density estimator and by generalizing noise power spectrum estimation.…”

“…12, we introduced a supervised method of BTC estimation for digital breast tomosynthesis (DBT) stacks. The differences between consecutive slices, measured in peak signal-to-noise ratio, were used as the input to the estimator.…”