&lt;title&gt;X-ray image system design using a human visual model&lt;/title&gt;

Jackson, Warren B.; Beebee, Peter; Jared, David A.; Biegelsen, D. K.; Larimer, James; Lubin, Jeffrey; Gille, Jennifer

doi:10.1117/12.237794

Cited by 16 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…15 The Visual Discrimination Model (VDM) model that we have used previously [12][13][14][15] is the JNDmetrix model (JND = Just Noticeable Difference) developed by the Sarnoff Corporation. [16][17] It predicts observer performance in visual discrimination tasks. It begins with two paired images as input and ends with a JND map showing the magnitude and location of visible differences between the input images.…”

Section: Methodsmentioning

confidence: 99%

Viewing images on and off axis with CRT and LCD monitors: effects on observer and model performance

et al. 2005

Self Cite

View full text Add to dashboard Cite

CRT displays are generally used for softcopy display in the digital reading room, but LCDs are being used more frequently. LCDs have many useful properties, but can suffer from significant degradation when viewed off-axis. We compared observer performance and human visual system model performance for on and off-axis CRT and LCD viewing. 400 mammographic regions of interest with different lesion contrasts were shown on and off-axis to radiologists on a CRT and LCD. Receiver Operating Characteristic (ROC) techniques were used to analyze observer performance and results were correlated with the predictions of the human vision model (JNDmetrix model). Both sets of performance metrics showed that LCD on-axis viewing was better than the CRT; and off-axis was significantly better with the CRT. Off-axis LCD viewing of radiographs can degrade observer performance compared to a CRT.

show abstract

Section: Methodsmentioning

confidence: 99%

Viewing images on and off axis with CRT and LCD monitors: effects on observer and model performance

et al. 2005

Self Cite

View full text Add to dashboard Cite

show abstract

“…[1][2] It is used to predict performance in visual discrimination tasks. It starts with two paired images as input and ends with a JND map showing the magnitude and location of visible differences between the input images.…”

Section: Methodsmentioning

confidence: 99%

Model prediction of just noticeable differences: Are the eyes attracted to the same areas?

et al. 2005

Self Cite

View full text Add to dashboard Cite

The JNDmetrix human visual system model developed by the Sarnoff Corporation is used to predict observer performance on visual discrimination tasks. It begins with two paired images as the initial input and ends with a JND map that shows the magnitude and spatial location of visible differences between the two input images. The goal of this experiment was to determine if the location and magnitude of JNDs identified by the model corresponded to visual search parameters of the human observer. Radiologists searched 20 mammograms with multiple masses and microcalcifications of different subtleties as their eye-position was recorded. The JNDmetrix model analyzed the same images and identified, with JNDs, discriminable areas on the images. Lesions with lower subtlety ratings were detected later in search than more obvious ones (FNs later than TPs). When the subtler lesions were detected (TP) dwell time was longer than more obvious lesions, but the FNs received shorter total dwell. The subtler lesions when detected (TP) received more total fixation clusters than more obvious ones, but the FNs received fewer. The correlation between the model JNDs and the eye-position parameters was high. Understanding the influence of lesion subtlety on search may help us better model and predict human observer performance.

show abstract

“…Applications of the VDM in medical imaging have been evaluated in numerous research projects over the past 10 years mostly in radiology [38,45–50], and using properties of the human visual system in a modeling approach and/or using perceptual metrics for evaluating the effects of image compression is not new [51–61]; but to our knowledge, these approaches have yet to be applied to telemedicine images in general or to telepathology images in particular. High degrees of correlation have been found between human diagnostic accuracy values and the VDM metrics for the same test images.…”

Section: Image Compressionmentioning

confidence: 99%

Virtual slide telepathology workstation of the future: lessons learned from teleradiology

Krupiński

2009

Human Pathology

View full text Add to dashboard Cite

Summary The clinical reading environment for the 21st century pathologist looks very different than it did even a few short years ago. Glass slides are quickly being replaced by digital “virtual slides,” and the traditional light microscope is being replaced by the computer display. There are numerous questions that arise however when deciding exactly what this new digital display viewing environment will be like. Choosing a workstation for daily use in the interpretation of digital pathology images can be a very daunting task. Radiology went digital nearly 20 years ago and faced many of the same challenges so there are lessons to be learned from these experiences. One major lesson is that there is no “one size fits all” workstation so users must consider a variety of factors when choosing a workstation. In this article, we summarize some of the potentially critical elements in a pathology workstation and the characteristics one should be aware of and look for in the selection of one. Issues pertaining to both hardware and software aspects of medical workstations will be reviewed particularly as they may impact the interpretation process.

show abstract

<title>X-ray image system design using a human visual model</title>

Cited by 16 publications

References 5 publications

Viewing images on and off axis with CRT and LCD monitors: effects on observer and model performance

Viewing images on and off axis with CRT and LCD monitors: effects on observer and model performance

Model prediction of just noticeable differences: Are the eyes attracted to the same areas?

Virtual slide telepathology workstation of the future: lessons learned from teleradiology

Contact Info

Product

Resources

About