It Is Hard to See a Needle in a Haystack: Modeling Contrast Masking Effect in a Numerical Observer

Avanaki, Ali R. N.; Espig, Kathryn S.; Xthona, Albert; Kimpe, Tom; Bakić, Predrag R.; Maidment, Andrew D. A.

doi:10.1007/978-3-319-07887-8_100

Cited by 3 publications

(12 citation statements)

References 12 publications

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“…The blocks within the dotted rectangle are introduced in this paper and described in the remainder of this section. The rest of the blocks have been explained previously [1,2,4].…”

Section: Methodsmentioning

confidence: 99%

“…To create datasets with varying background complexity, spatiotemporal low-pass Gaussian noise with four different levels of energy was added to the dataset generated by the UPenn phantoms [4].…”

Section: Methodsmentioning

confidence: 99%

“…Among the six varieties of the anthropomorphic model observer that we have proposed [4], "PM+masking" caused the largest change in AUC (from 97% to 92%; d' from 2.66 to 1.99) * when background complexity changed from level 0 (simple) to 4 (very complex; several lesion-like objects visible). For the same change in background complexity, the performance of human observers dropped considerably more (from AUC = 98.6% to 67.5% and d'=3.11 to 0.64 for observer A; from 92.6% to 60.6% and d' = 2.05 to 0.38 for observer B) [4].…”

Section: Human Reader Error Analysismentioning

confidence: 99%

“…In this paper, we explore an alternative to the existing decision block of a traditional observer pipeline. This alternative is designed based upon our analysis of the missed detection cases in our previous reader [4] and thus allows for better prediction of human observers' performance with increasing background complexity.…”

Section: Introductionmentioning

confidence: 99%

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On anthropomorphic decision making in a model observer

Avanaki¹,

Espig²,

Kimpe

et al. 2015

SPIE Proceedings

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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.

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“…The blocks within the dotted rectangle are introduced in this paper and described in the remainder of this section. The rest of the blocks have been explained previously [1,2,4].…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Human Reader Error Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On anthropomorphic decision making in a model observer

Avanaki¹,

Espig²,

Kimpe

et al. 2015

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

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“…This is because the human observer's performance, unlike that of the existing model observers, varies considerably with BTC in a signal that is exactly known and a location that is exactly known (SKE/LKE) simulation paradigm. 7 A model observer's capability of tracking the human observer performance with BTC is especially important when using VCT for design and/or optimization of contrast-enhanced (CE) breast imaging systems. In such systems, the uptake of the lesion relative to that of the normal fibroglandular tissue and the dose of injected contrast agent affect the perceived BTC and thus lesion visibility.…”

Section: Introductionmentioning

confidence: 99%

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

Avanaki¹,

Espig²,

Kimpe

2017

J. Med. Imag

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Abstract. We specify a notion of perceived background tissue complexity (BTC) that varies with lesion shape, lesion size, and lesion location in the image. We propose four unsupervised BTC estimators based on: perceived pre and postlesion similarity of images, lesion border analysis (LBA; conspicuous lesion should be brighter than its surround), tissue anomaly detection, and local energy. The latter two are existing methods adapted for location-and lesion-dependent BTC estimation. For evaluation, we ask human observers to measure BTC (threshold visibility amplitude of a given lesion inserted) at specified locations in a mammogram. As expected, both human measured and computationally estimated BTC vary with lesion shape, size, and location. BTCs measured by different human observers are correlated (ρ ¼ 0.67). BTC estimators are correlated to each other (0.84 < ρ < 0.95) and less so to human observers (ρ ≤ 0.81). With change in lesion shape or size, LBA estimated BTC changes in the same direction as human measured BTC. Proposed estimators can be generalized to other modalities (e.g., breast tomosynthesis) and used as-is or customized to a specific human observer, to construct BTC-aware model observers with applications, such as optimization of contrast-enhanced medical imaging systems and creation of a diversified image dataset with characteristics of a desired population.

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