Biomedical subjects

Craig K Abbey

Publications and source records attributed to Craig K Abbey.

15 recordsLinked to original sources

PubMed2004

In vivo positron-emission tomography imaging of progression and transformation in a mouse model of mammary neoplasia.

Imaging mouse models of human cancer promises more effective analysis of tumor progression and reduction of the number of animals needed for statistical power in preclinical therapeutic intervention trials. This study utilizes positron emission tomography imaging of 2-[18F]-fluoro-deoxy-D-glucose to monitor longitudinal development of mammary intraepithelial neoplasia outgrowths in immunocompetent FVB/NJ mice. The mammary intraepithelial neoplasia outgrowth tissues mimic the progression of breast cancer from premalignant ductal carcinoma in situ to invasive carcinoma. Progression of disease is clearly evident in the positron emission tomography images, and tracer uptake correlates with histological evaluation. Furthermore, quantitative markers of disease extracted from the images can be used to track proliferation and progression in vivo over multiple time points.

Animals↗

PubMed2004

Search for lesions in mammograms: statistical characterization of observer responses.

We investigate human performance for visually detecting simulated microcalcifications and tumors embedded in x-ray mammograms as a function of signal contrast and the number of possible signal locations. Our results show that performance degradation with an increasing number of locations is well approximated by signal detection theory (SDT) with the usual Gaussian assumption. However, more stringent statistical analysis finds a departure from Gaussian assumptions for the detection of microcalcifications. We investigated whether these departures from the SDT Gaussian model could be accounted for by an increase in human internal response correlations arising from the image-pixel correlations present in 1/f spectrum backgrounds and/or observer internal response distributions that departed from the Gaussian assumption. Results were consistent with a departure from the Gaussian response distributions and suggested that the human observer internal responses were more compact than the Gaussian distribution. Finally, we conducted a free search experiment where the signal could appear anywhere within the image. Results show that human performance in a multiple-alternative forced-choice experiment can be used to predict performance in the clinically realistic free search experiment when the investigator takes into account the search area and the observers' inherent spatial imprecision to localize the targets.

Female↗

PubMed2004

Linear approach to axial resolution in elasticity imaging.

Thus far axial resolution in elasticity imaging has been addressed only empirically. No clear analytical approaches have emerged because the estimator is non-linear in the data, correlation functions are nonstationary, and system responses vary spatially. This paper describes a linear systems approach based on a small-strain impulse approximation that results in the derivation of a local impulse response (LIR) and local modulation transfer function (LMTF). Closed-form solutions for strain LIR are available to provide new insights on the role of instrumentation and processing on axial strain resolution. Novel phantom measurements are generated to validate results. We found that the correlation window determines axial resolution in most practical situations, but that the the same system properties that determine B-mode resolution ultimately limit elasticity imaging.

Animals↗

PubMed2003

Ideal observer model for detection of blood perfusion and flow using ultrasound.

An ideal observer model is developed for the task of detecting blood perfusing or flowing through tissue. The ideal observer theory relies on a linear systems model that describes tissue and blood object functions and electronic noise as random processes. When aliasing is minimal, the system is characterized by a quantity similar to Noise-Equivalent Quanta used in photon imaging modalities. A simple 1-D model is used to illustrate the effect of the system and object parameters on task performance. Velocity and decorrelation are seen to be advantageous for detection. Aliasing can degrade performance. The ideal observer model provides a framework for assessing the performance of Power Doppler ultrasound systems, and may aid in their design.

Algorithms↗

PubMed2003

Linear system models for ultrasonic imaging: application to signal statistics.

Linear equations for modeling echo signals from shift-variant systems forming ultrasonic B-mode, Doppler, and strain images are analyzed and extended. The approach is based on a solution to the homogeneous wave equation for random inhomogeneous media. When the system is shift-variant, the spatial sensitivity function--defined as a spatial weighting function that determines the scattering volume for a fixed point of time--has advantages over the point-spread function traditionally used to analyze ultrasound systems. Spatial sensitivity functions are necessary for determining statistical moments in the context of rigorous image quality assessment, and they are time-reversed copies of point-spread functions for shift variant systems. A criterion is proposed to assess the validity of a local shift-invariance assumption. The analysis reveals realistic situations in which in-phase signals are correlated to the corresponding quadrature signals, which has strong implications for assessing lesion detectability. Also revealed is an opportunity to enhance near- and far-field spatial resolution by matched filtering unfocused beams. The analysis connects several well-known approaches to modeling ultrasonic echo signals.

Computer Simulation↗

PubMed2003

Computer aided detection of masses in mammography using subregion Hotelling observers.

We propose to investigate the use of the subregion Hotelling observer for the basis of a computer aided detection scheme for masses in mammography. A database of 1320 regions of interest (ROIs) was selected from the DDSM database collected by the University of South Florida using the Lumisys scanner cases. The breakdown of the cases was as follows: 656 normal ROIs, 307 benign ROIs, and 357 cancer ROIs. Each ROI was extracted at a size of 1024 x 1024 pixels and sub-sampled to 128 x 128 pixels. For the detection task, cancer and benign cases were considered positive and normal was considered negative. All positive cases had the lesion centered in the ROI. We chose to investigate the subregion Hotelling observer as a classifier to detect masses. The Hotelling observer incorporates information about the signal, the background, and the noise correlation for prediction of positive and negative and is the optimal detector when these are known. For our study, 225 subregion Hotelling observers were set up in a 15 x 15 grid across the center of the ROIs. Each separate observer was designed to "observe," or discriminate, an 8 x 8 pixel area of the image. A leave one out training and testing methodology was used to generate 225 "features," where each feature is the output of the individual observers. The 225 features derived from separate Hotelling observers were then narrowed down by using forward searching linear discriminants (LDs). The reduced set of features was then analyzed using an additional LD with receiver operating characteristic (ROC) analysis. The 225 Hotelling observer features were searched by the forward searching LD, which selected a subset of 37 features. This subset of 37 features was then analyzed using an additional LD, which gave a ROC area under the curve of 0.9412 +/- 0.006 and a partial area of 0.6728. Additionally, at 98% sensitivity the overall classifier had a specificity of 55.9% and a positive predictive value of 69.3%. Preliminary results suggest that using subregion Hotelling observers in combination with LDs can provide a strong backbone for a CAD scheme to help radiologists with detection. Such a system could be used in conjunction with CAD systems for false positive reduction.

Algorithms↗

PubMed2003

Comparison of two weighted integration models for the cueing task: linear and likelihood.

In a task in which the observer must detect a signal at two locations, presenting a precue that predicts the location of a signal leads to improved performance with a valid cue (signal location matches the cue), compared to an invalid cue (signal location does not match the cue). The cue validity effect has often been explained with a limited capacity attentional mechanism improving the perceptual quality at the cued location. Alternatively, the cueing effect can also be explained by unlimited capacity models that assume a weighted combination of noisy responses across the two locations. We compare two weighted integration models, a linear model and a sum of weighted likelihoods model based on a Bayesian observer. While qualitatively these models are similar, quantitatively they predict different cue validity effects as the signal-to-noise ratios (SNR) increase. To test these models, 3 observers performed in a cued discrimination task of Gaussian targets with an 80% valid precue across a broad range of SNR's. Analysis of a limited capacity attentional switching model was also included and rejected. The sum of weighted likelihoods model best described the psychophysical results, suggesting that human observers approximate a weighted combination of likelihoods, and not a weighted linear combination.

Adult↗

PubMed2003

An ideal observer with channels versus feature-independent processing of spatial frequency and orientation in visual search performance.

An influential assumption for the front end of models in vision, visual search, and object recognition is an analysis of independent features that correspond to basic image properties, such as motion, shape, and color. Empirically, one common test of independent features (a cue-summation study) measures performance with increasing available cues or features, with improving performance leading to conclusions of summation across independent features. In a study by Shimozaki et al. [J. Vision 2, 354-370 (2002)], both ideal and human observers showed no summation with large stimulus differences, in contrast to independent-feature models and suggesting that stimulus information (as assessed by an ideal observer) might affect cue-summation studies. Extending the previous summation study, observers performed a visual search of four Gabors differing in only orientation, only spatial frequency, or both orientation and spatial frequency, across a range of target-distractor differences. An ideal observer underpredicted human summation for small differences, whereas the independent-orientation and spatial-frequency feature models overpredicted human summation for large differences. An ideal observer with channels jointly tuned to spatial frequency and orientation predicted human performance across both small and large target-distractor differences.

Cues↗

PubMed2002

Improved localization of coronary stents using layer decomposition.

Accurate placement and expansion of coronary stents is hindered by the fact that most stents are only slightly radiopaque and hence difficult to see in typical coronary X-ray images. We propose a new technique for improved image guidance of multiple coronary stent deployment using layer decomposition of coronary X-ray image sequences. We hypothesize that layer decomposition can improve the accuracy of localization of the end of a deployed stent. Layer decomposition is used to obtain good quality images of a stent in vitro. The resultant background-subtracted stent images are embedded into other cine X-ray image sequences to form a database of simulated image sequences. For each simulated sequence, the position of the stent edge is estimated from raw and layer-decomposed images using a small region of the original layer image as a template. Layer decomposition reduced median position errors in 33 of 47 image sequences (70%), including 16 of 18 sequences in which the position errors for raw and layer images differed by 5.0 pixels (0.5 mm) or more. Layer decomposition significantly reduces errors in determination of stent edge location in simulated cine X-ray image sequences.

Cineangiography↗

PubMed2002

Validation of the localization of the target tissue for intracoronary brachytherapy.

In a previous meta-analysis of intracoronary brachytherapy (ICBT) studies, we identified the target tissue at 0.6 to 0.7 mm tissue depth and we developed two models, describing the relationship between dose and ICBT effectiveness. The purpose of the present study was to validate the identified target tissue depth and the developed dose models, using the results of 1) two prospective animal studies with ICBT, 2) a retrospective analysis of animal studies with external beam irradiation and 3) results of recent clinical ICBT trials. ICBT effectiveness in the porcine restenosis studies was quantified as inhibition of neointima proliferation. The results of these studies were correlated with the developed dose-effectiveness model. Finally, the agreement of the restenosis rates of the recent clinical trials with the developed dose-restenosis model was tested. The porcine restenosis studies demonstrated a dose-related inhibition of neointima proliferation. The radiation effectiveness of both prospective studies and the effectiveness of the studies with external beam irradiation demonstrated the best agreement with the developed dose model at a tissue depth of 0.6 mm. Furthermore, the restenosis rates of the recent clinical ICBT studies were in concordance with the developed dose-restenosis model. In conclusion, the current study validated the localization of the target tissue for ICBT at a tissue depth of 0.6 to 0.7 mm as well as the relationship between dose and ICBT effectiveness at this depth. The data provide a rationale for setting a common dose prescription point at 0.6 to 0.7 mm tissue depth.

Angioplasty, Balloon, Coronary↗

PubMed2002

Optimal shifted estimates of human-observer templates in two-alternative forced-choice experiments.

For performing simple detection and discrimination tasks in image noise, human observers are often modeled by a cross-correlation between the image and an observer template followed by the injection of the observer's internal noise. This paper is concerned with estimating this template using the two-alternative forced-choice (2AFC) experimental paradigm. The basic idea behind the estimation procedure is to average the noise fields of the images used in a 2AFC experiment with a weight that depends on whether the observer got the trial correct or incorrect. We describe a method that produces unbiased estimates of the observer template up to a constant of proportionality under the linear cross-correlation model. The method proposed here is different from some previous methods in the way it assigns weights to the noise fields and we show that the resulting errors in the estimated template are minimized. We also propose and validate a formula for approximating the error covariance associated with the template estimates.

Choice Behavior↗

PubMed2002

Evaluation of layer decomposition for multiframe quantitative coronary angiography.

Multiframe quantitative coronary angiography is typically performed by averaging measurements of artery diameter over multiple frames. This approach reduces errors attributable to random noise but may not reduce systematic errors caused by background structures, nonlinear system response, and motion blur. We attempt to reduce these sources of error by decomposing the image sequence into moving layers, one of which includes the artery. We embed simulated arteries into clinical angiographic sequences so that the true vessel dimensions are known accurately. The measurement tasks are minimum diameter, geometric percent stenosis, and densitometric percent stenosis. We compare measurements for single and multiple raw images, single images with fixed mask subtraction, single and multiple images with layered background subtraction, and time-averaged layer images. We find that both multiframe averaging and layer decomposition significantly improve geometric and densitometric accuracy compared with single-frame measurements. The best results were obtained by averaging measurements from multiple frames of layered background-subtracted images.

Algorithms↗

PubMed2002

The footprints of visual attention in the Posner cueing paradigm revealed by classification images.

In the Posner cueing paradigm, observers' performance in detecting a target is typically better in trials in which the target is present at the cued location than in trials in which the target appears at the uncued location. This effect can be explained in terms of a Bayesian observer where visual attention simply weights the information differently at the cued (attended) and uncued (unattended) locations without a change in the quality of processing at each location. Alternatively, it could also be explained in terms of visual attention changing the shape of the perceptual filter at the cued location. In this study, we use the classification image technique to compare the human perceptual filters at the cued and uncued locations in a contrast discrimination task. We did not find statistically significant differences between the shapes of the inferred perceptual filters across the two locations, nor did the observed differences account for the measured cueing effects in human observers. Instead, we found a difference in the magnitude of the classification images, supporting the idea that visual attention changes the weighting of information at the cued and uncued location, but does not change the quality of processing at each individual location.

Attention↗

PubMed2002

Classification image analysis: estimation and statistical inference for two-alternative forced-choice experiments.

We consider estimation and statistical hypothesis testing on classification images obtained from the two-alternative forced-choice experimental paradigm. We begin with a probabilistic model of task performance for simple forced-choice detection and discrimination tasks. Particular attention is paid to general linear filter models because these models lead to a direct interpretation of the classification image as an estimate of the filter weights. We then describe an estimation procedure for obtaining classification images from observer data. A number of statistical tests are presented for testing various hypotheses from classification images based on some more compact set of features derived from them. As an example of how the methods we describe can be used, we present a case study investigating detection of a Gaussian bump profile.

Choice Behavior↗

PubMed2002

Stimulus information contaminates summation tests of independent neural representations of features.

Many models of visual processing assume that visual information is analyzed into separable and independent neural codes, or features. A common psychophysical test of independent features is known as a summation study, which measures performance in a detection, discrimination, or visual search task as the number of proposed features increases. Improvement in human performance with increasing number of available features is typically attributed to the summation, or combination, of information across independent neural coding of the features. In many instances, however, increasing the number of available features also increases the stimulus information in the task, as assessed by an optimal observer that does not include the independent neural codes. In a visual search task with spatial frequency and orientation as the component features, a particular set of stimuli were chosen so that all searches had equivalent stimulus information, regardless of the number of features. In this case, human performance did not improve with increasing number of features, implying that the improvement observed with additional features may be due to stimulus information and not the combination across independent features.

Humans↗