A Virtual Image Chain for Perceived and Clinical Image Quality of Medical Display

Marchessoux, Cédric; Kimpe, Tom; Bert, Tom

doi:10.1109/jdt.2008.2001164

Cited by 19 publications

(14 citation statements)

References 19 publications

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“…Second, during the testing phase, the estimated template is applied to a set of testing images to generate ratings, from which a performance estimate is obtained. Due to frequent use of training/testing in CHO performance evaluations, e.g., [11], [12], [14], [16]–[18], [20], [25], it is worthwhile to clarify the relationship between the approach of this paper and the training/testing paradigm.…”

Section: Comparison To Training/testing Approachesmentioning

confidence: 99%

Exact Confidence Intervals for Channelized Hotelling Observer Performance in Image Quality Studies

Wunderlich

Noo

Gallas

et al. 2015

IEEE Trans. Med. Imaging

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Task-based assessments of image quality constitute a rigorous, principled approach to the evaluation of imaging system performance. To conduct such assessments, it has been recognized that mathematical model observers are very useful, particularly for purposes of imaging system development and optimization. One type of model observer that has been widely applied in the medical imaging community is the channelized Hotelling observer (CHO), which is well-suited to known-location discrimination tasks. In the present work, we address the need for reliable confidence interval estimators of CHO performance. Specifically, we show that the bias associated with point estimates of CHO performance can be overcome by using confidence intervals proposed by Reiser for the Mahalanobis distance. In addition, we find that these intervals are well-defined with theoretically-exact coverage probabilities, which is a new result not proved by Reiser. The confidence intervals are tested with Monte Carlo simulation and demonstrated with two examples comparing X-ray CT reconstruction strategies. Moreover, commonly-used training/testing approaches are discussed and compared to the exact confidence intervals. MATLAB software implementing the estimators described in this work is publicly available at http://code.google.com/p/iqmodelo/.

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Section: Comparison To Training/testing Approachesmentioning

confidence: 99%

Exact Confidence Intervals for Channelized Hotelling Observer Performance in Image Quality Studies

Wunderlich

Noo

Gallas

et al. 2015

IEEE Trans. Med. Imaging

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“…The scale between the video input and the intensity map is thus 1:1, there is no interpolation between the two. For this study, the virtual display is a simplified version of the chain described in 8 . The simulation can be broken down in two parts: a signal processing block and a panel characterization block.…”

Section: Display Simulationmentioning

confidence: 99%

Perceptually optimal compression for heterogeneous image content in the context of medical networked applications

et al. 2010

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In medical networked applications, the server-generated application view, consisting of medical image content and synthetic text/GUI elements, must be compressed and transmitted to the client. To adapt to the local content characteristics, the application view is divided into rectangular patches, which are classified into content classes: medical image patches, synthetic image patches consisting of text on a uniform/natural/medical image background and synthetic image patches consisting of GUI elements on a uniform/natural/medical image background. Each patch is thereafter compressed using a technique yielding perceptually optimal performance for the identified content class. The goal of this paper is to identify this optimal technique, given a set of candidate schemes. For this purpose, a simulation framework is used which simulates different types of compression and measures the perceived differences between the compressed and original images, taking into account the display characteristics. In a first experiment, JPEG is used to code all patches and the optimal chroma subsampling and quantization parameters are derived for different content classes. The results show that 4:4:4 chroma subsampling is the best choice, regardless of the content type. Furthermore, frequency dependant quantization yields better compression performance than uniform quantization, except for content containing a significant number of very sharp edges. In a second experiment, each patch can be coded using JPEG, JPEG XR or JPEG 2000. On average, JPEG 2000 outperforms JPEG and JPEG XR for most medical images and for patches containing text. However, for histopathology or tissue patches and for patches containing GUI elements, classical JPEG compression outperforms the other two techniques.

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“…It is of high importance that the MO selected is able to show similar detectability trends to humans, so that in the future human observer performance could be predicted from the MO results as part of QC and optimization procedures (acquisition techniques and postprocessing optimization). As shown by Marchessoux et al and Avanaki et al, the effect of the image display is also important to consider when comparing human and model observer performance in order to include the information provided to both observers . However, evidence on the extent to which the CHO can predict human performance for different DM systems, including image processing and visualization on the monitor, is lacking.…”

Section: Introductionmentioning

confidence: 99%

“…As shown by Marchessoux et al and Avanaki et al, the effect of the image display is also important to consider when comparing human and model observer performance in order to include the information provided to both observers. 18,19 However, evidence on the extent to which the CHO can predict human performance for different DM systems, including image processing and visualization on the monitor, is lacking.…”

Section: Introductionmentioning

confidence: 99%

Can a channelized Hotelling observer assess image quality in acquired mammographic images of an anthropomorphic breast phantom including image processing?

et al. 2019

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Purpose To study the feasibility of a channelized Hotelling observer (CHO) to predict human observer performance in detecting calcification‐like signals in mammography images of an anthropomorphic breast phantom, as part of a quality control (QC) framework. Methods A prototype anthropomorphic breast phantom with inserted gold disks of 0.25 mm diameter was imaged with two different digital mammography x‐ray systems at four different dose levels. Regions of interest (ROIs) were extracted from the acquired processed and unprocessed images, signal‐present and signal‐absent. The ROIs were evaluated by a CHO using four different formulations of the difference of Gaussian (DoG) channel sets. Three human observers scored the ROIs in a two‐alternative forced‐choice experiment. We compared the human and the CHO performance on the simple task to detect calcification‐like disks in ROIs with and without postprocessing. The proportion of correct responses of the human reader (PCH) and the CHO (PCCHO) was calculated and the correlation between the two was analyzed using a mixed‐effect regression model. To address the signal location uncertainty, the impact of shifting the DoG channel sets in all directions up to two pixels was evaluated. Correlation results including the goodness of fit (r2) of PCH and PCCHO for all different parameters were evaluated. Results Subanalysis by system yielded strong correlations between PCH and PCCHO, with r2 between PCH and PCCHO was found to be between 0.926 and 0.958 for the unshifted and between 0.759 and 0.938 for the shifted channel sets, respectively. However, the linear fit suggested a slight system dependence. PCCHO with shifted channel sets increased CHO performance but the correlation with humans was decreased. These correlations were not considerably affected by of the DoG channel set used. Conclusions There is potential for the CHO to be used in QC for the evaluation of detectability of calcification‐like signals. The CHO can predict the PC of humans in images of calcification‐like signals of two different systems. However, a global model to be used for all systems requires further investigation.

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A Virtual Image Chain for Perceived and Clinical Image Quality of Medical Display

Cited by 19 publications

References 19 publications

Exact Confidence Intervals for Channelized Hotelling Observer Performance in Image Quality Studies

Exact Confidence Intervals for Channelized Hotelling Observer Performance in Image Quality Studies

Perceptually optimal compression for heterogeneous image content in the context of medical networked applications

Can a channelized Hotelling observer assess image quality in acquired mammographic images of an anthropomorphic breast phantom including image processing?

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