Biomedical subjects

Yani Zhang

Publications and source records attributed to Yani Zhang.

5 recordsLinked to original sources

PubMed2006

The effect of nonlinear human visual system components on performance of a channelized Hotelling observer in structured backgrounds.

Linear model observers based on statistical decision theory have been used successfully to predict human visual detection of aperiodic signals in a variety of noisy backgrounds. However, some models have included nonlinearities such as a transducer or nonlinear decision rules to handle intrinsic uncertainty. In addition, masking models used to predict human visual detection of signals superimposed on one of two identical backgrounds (masks) usually include a number of nonlinear components in the channels that reflect properties of the firing of cells in the primary visual cortex (V1). The effect of these nonlinearities on the ability of linear model observers to predict human signal detection in real patient structured backgrounds is unknown. We evaluate the effect of including different nonlinear human visual system components into a linear channelized Hotelling observer (CHO) using a signal known exactly but variable (SKEV) task. In particular, we evaluate whether the rank order of two compression algorithms (JPEG versus JPEG 2000) and two compression encoder settings (JPEG 2000 default versus JPEG 2000 optimized) based on model observer signal detection performance in X-ray coronary angiograms is altered by inclusion of nonlinear components. The results show: 1) the simpler linear CHO model observer outperforms CHO model with the nonlinear components; 2) the rank order of model observer performance for the compression algorithms/parameters does not change when the nonlinear components are included. For the present task and images, the results suggest that the addition of the nonlinearities to a channelized Hotelling model may add complexity to the model observers without great impact on rank order evaluation of image processing and/or acquisition algorithms.

Algorithms↗

PubMed2006

Adaptive detection mechanisms in globally statistically nonstationary-oriented noise.

Studies have shown that human observers can adapt their detection strategies on the basis of the statistical properties of noisy backgrounds. One common property of such studies is that the backgrounds studied are (or are assumed to be) statistically stationary. Less is known about how humans detect signals in the more complex setting of nonstationary backgrounds. We investigated detection performance in the presence of a globally nonstationary oriented noise background. We controlled for noise-correlation effects by considering a stationary background with a power spectrum matched to the average spectrum of the nonstationary process. Performance of a nonadaptive linear filter that was unable to make use of differences in local statistics yielded constant performance in both the stationary and the nonstationary backgrounds. In contrast, performance of an ideal observer that uses local noise statistics yielded substantially higher (140%) detectability with the nonstationary backgrounds than the stationary ones. Human observers showed significantly higher (33%) detection performance in the nonstationary backgrounds, suggesting that they can adapt their detection mechanisms to the local orientation properties.

Journal Article↗

PubMed2005

Task-based model/human observer evaluation of SPIHT wavelet compression with human visual system-based quantization.

RATIONALE AND OBJECTIVE: The set partitioning in hierarchical trees (SPIHT) wavelet image compression algorithm with the human visual system (HVS) quantization matrix was investigated using x-ray coronary angiograms. We tested whether the HVS quantization matrix for the SPIHT wavelet compression improved computer model/human observer performance in a detection task with variable signals compared to performance with the default quantization matrix. We also tested the hypothesis of whether evaluating the rank order of the two quantization matrices (HVS versus default) based on performance of computer model observers in a signal known exactly but variable task (SKEV) generalized to model/human performance in the more clinically realistic signal known statistically task (SKS). MATERIALS AND METHODS: Nine hundred test images were created using real x-ray coronary angiograms as backgrounds and simulated arteries with filling defects (signals). The task for the model and human observer was to detect which one of the four computer simulated arterial segments contained the signal, four alternative-forced-choice (4 AFC). We obtained performance for four model observers (nonprewhitening matched filter with an eye filter, Hotelling, Channelized Hotelling, and Laguerre Gauss Hotelling model observers) for both the SKEV and SKS tasks with images compressed with and without the HVS quantization matrix. A psychophysical study measured performance from three human observers for the same conditions and tasks as the model observers. RESULTS: Performance for all four model observers improved with the use of the HVS quantization scheme. Improvements ranged from 5% (at compression ratio 7:1) to 50% (at compression ratio 30:1) for both the SKEV and SKS tasks. Human observer performance improvement averaged across observers ranged from 6% (at compression ratio 7:1) to 35% (at compression ratio 30:1) for the SKEV task and from 2% (at compression ratio 7:1) to 38% (at compression ratio 30:1) for the SKS task. Addition of internal noise to the model observers allowed for good prediction of human performance. CONCLUSIONS: Use of the HVS quantization scheme in the SPIHT wavelet compression led to improved model and human observer performance in clinically relevant detection tasks in x-ray coronary angiograms. Model observer performance can be reliably used to predict the human observer performance for the studied tasks as a function of SPIHT wavelet image compression. Our results further confirmed that model observer performance in the computationally more tractable SKEV task can be potentially used as a figure of merit for the more clinically realistic SKS task with real anatomic backgrounds.

Algorithms↗

PubMed2004

Automated optimization of JPEG 2000 encoder options based on model observer performance for detecting variable signals in X-ray coronary angiograms.

Image compression is indispensable in medical applications where inherently large volumes of digitized images are presented. JPEG 2000 has recently been proposed as a new image compression standard. The present recommendations on the choice of JPEG 2000 encoder options were based on nontask-based metrics of image quality applied to nonmedical images. We used the performance of a model observer [non-prewhitening matched filter with an eye filter (NPWE)] in a visual detection task of varying signals [signal known exactly but variable (SKEV)] in X-ray coronary angiograms to optimize JPEG 2000 encoder options through a genetic algorithm procedure. We also obtained the performance of other model observers (Hotelling, Laguerre-Gauss Hotelling, channelized-Hotelling) and human observers to evaluate the validity of the NPWE optimized JPEG 2000 encoder settings. Compared to the default JPEG 2000 encoder settings, the NPWE-optimized encoder settings improved the detection performance of humans and the other three model observers for an SKEV task. In addition, the performance also was improved for a more clinically realistic task where the signal varied from image to image but was not known a priori to observers [signal known statistically (SKS)]. The highest performance improvement for humans was at a high compression ratio (e.g., 30:1) which resulted in approximately a 75% improvement for both the SKEV and SKS tasks.

Algorithms↗

PubMed2004

Evaluation of JPEG 2000 encoder options: human and model observer detection of variable signals in X-ray coronary angiograms.

Previous studies have evaluated the effect of the new still image compression standard JPEG 2000 using nontask based image quality metrics, i.e., peak-signal-to-noise-ratio (PSNR) for nonmedical images. In this paper, the effect of JPEG 2000 encoder options was investigated using the performance of human and model observers (nonprewhitening matched filter with an eye filter, square-window Hotelling, Laguerre-Gauss Hotelling and channelized Hotelling model observer) for clinically relevant visual tasks. Two tasks were investigated: the signal known exactly but variable task (SKEV) and the signal known statistically task (SKS). Test images consisted of real X-ray coronary angiograms with simulated filling defects (signals) inserted in one of the four simulated arteries. The signals varied in size and shape. Experimental results indicated that the dependence of task performance on the JPEG 2000 encoder options was similar for all model and human observers. Model observer performance in the more tractable and computationally economic SKEV task can be used to reliably estimate performance in the complex but clinically more realistic SKS task. JPEG 2000 encoder settings different from the default ones resulted in greatly improved model and human observer performance in the studied clinically relevant visual tasks using real angiography backgrounds.

Algorithms↗