Validating the use of channels to estimate the ideal linear observer

Gallas, Brandon D.; Barrett, Harrison H.

doi:10.1364/josaa.20.001725

Cited by 137 publications

(173 citation statements)

References 21 publications

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“…One very successful class of model observers are known as Channelized Hotelling observers (CHOs) [2,3], which are an extension of the Hotelling observer that is tractable when the target profile and background statistics are presumed known or can be estimated [4]. CHO models use channels as a set of linear responses, which are then combined to form a decision variable, using Fisher-Hotelling weights.…”

mentioning

confidence: 99%

Derivation of an Observer Model Adapted to Irregular Signals Based on Convolution Channels

Díaz

Abbey

Timberg

et al. 2015

IEEE Trans. Med. Imaging

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Abstract-Anthropomorphic model observers are mathematical algorithms which are applied to images with the ultimate goal of predicting human signal detection and classification accuracy across varieties of backgrounds, image acquisitions and display conditions. A limitation of current channelized model observers is their inability to handle irregularly-shaped signals, which are common in clinical images, without a high number of directional channels. Here, we derive a new linear model observer based on convolution channels which we refer to as the "Filtered Channel observer" (FCO), as an extension of the channelized Hotelling observer (CHO) and the nonprewhitening with an eye filter (NPWE) observer. In analogy to the CHO, this linear model observer can take the form of a single template with an external noise term. To compare with human observers, we tested signals with irregular and asymmetrical shapes spanning the size of lesions down to those of microcalfications in 4-AFC breast tomosynthesis detection tasks, with three different contrasts for each case. Whereas humans uniformly outperformed conventional CHOs, the FCO observer outperformed humans for every signal with only one exception. Additive internal noise in the models allowed us to degrade model performance and match human performance. We could not match all the human performances with a model with a single internal noise component for all signal shape, size and contrast conditions. This suggests that either the internal noise might vary across signals or that the model cannot entirely capture the human detection strategy. However, the FCO model offers an efficient way to apprehend human observer performance for a non-symmetric signal.

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mentioning

confidence: 99%

Derivation of an Observer Model Adapted to Irregular Signals Based on Convolution Channels

Díaz

Abbey

Timberg

et al. 2015

IEEE Trans. Med. Imaging

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“…n I is then estimated in the training phase of the model [7]. Either test statistic (23) or (24) can then be used in order to estimate the area under the ROC (AUC) as a measure of detectability, e.g., using the Wilcoxon-AUC test [16].…”

Section: Estimating the Signal Detectabilitymentioning

confidence: 99%

“…As a general means to estimate the detectability SNR, while alleviating some of the previous problems, numerical observer models have been proposed [1,7]. Some examples are the (non)prewhitening matched filter, the Bayesian ideal observer and the (channelized) Hotelling observer.…”

Section: Introductionmentioning

confidence: 99%

“…In the image quality assessment application in the above example, this is not the case, because the signal x in (4) corresponds to a (possibly non-linearly) tomographically reconstructed signal. The channelized Hotelling observer (CHO) [7] circumvents this problem by estimating x and C b directly from the reconstructed images. To do so, a dimensionality reduction is applied by projecting onto a set of channels.…”

Section: Introductionmentioning

confidence: 99%

“…To do so, a dimensionality reduction is applied by projecting onto a set of channels. Although the CHO may an accurate estimate of the ideal Bayesian observer SNR [7] and serves as a no-reference based QA method, the CHO is unable to provide estimates of individual image quality factors. Recently, we have extended the CHO model to signal-knownstatistically (SKS) detection tasks [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Optimizing image quality using test signals: Trading off blur, noise and contrast

Goossens

Luong

Platiša

et al. 2012

2012 Fourth International Workshop on Quality of Multimedia Experience

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Objective image quality assessment (QA) is crucial in order to improve imaging systems and image processing techniques. In medical imaging, model observers that estimate signal detectability, have become widespread and promising as a means to avoid costly human observer experiments. However, signal detectability alone does not give the complete picture: one may also be interested in optimizing several independent quality factors (e.g. contrast, spatial resolution, noise).In recent work, we have proposed the channelized joint observer (CJO), to jointly detect and estimate random parametric signals in images, a so-called signal-known-statistically (SKS) detection task. In this paper, we show how the estimation capabilities of the CJO can be exploited to estimate several image quality factors in degraded images, through signal insertion. By fixing the signal detectability, we illustrate how to benefit from the trade-offs that exist between the different quality factors. Our method is in the first place intended to aid medical image reconstruction techniques and medical display design, although the technique can also be useful in a much wider context.

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Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial

et al. 2018

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Key Points Question Can in silico imaging trials play a role in the evaluation of new medical imaging systems? Findings This diagnostic study used computer-simulated imaging of 2986 synthetic image–based virtual patients to compare digital mammography and digital breast tomosynthesis and found an improved lesion detection performance favoring tomosynthesis for all breast sizes and lesion types. The increased performance for tomosynthesis was consistent with results from a comparative trial using human patients and radiologists. Meaning The study’s findings suggest that in silico imaging trials and imaging system computer simulation tools can in some cases be considered viable sources of evidence for the regulatory evaluation of imaging devices.

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Validating the use of channels to estimate the ideal linear observer

Cited by 137 publications

References 21 publications

Derivation of an Observer Model Adapted to Irregular Signals Based on Convolution Channels

Derivation of an Observer Model Adapted to Irregular Signals Based on Convolution Channels

Optimizing image quality using test signals: Trading off blur, noise and contrast

Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial

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