Showing papers by "Craig K. Abbey published in 2007"

Effect of dose reduction on the detection of mammographic lesions: a mathematical observer model analysis.

Abstract: The effect of reduction in dose levels normally used in mammographic screening procedures on the detection of breast lesions were analyzed. Four types of breast lesions were simulated and inserted into clinically-acquired digital mammograms. Dose reduction by 50% and 75% of the original clinically-relevant exposure levels were simulated by adding corresponding simulated noise into the original mammograms. The mammograms were converted into luminance values corresponding to those displayed on a clinical soft-copy display station and subsequently analyzed by Laguerre-Gauss and Gabor channelized Hotelling observer models for differences in detectability performance with reduction in radiation dose. Performance was measured under a signal known exactly but variable detection task paradigm in terms of receiver operating characteristics (ROC) curves and area under the ROC curves. The results suggested that luminance mapping of digital mammograms affects performance of model observers. Reduction in dose levels by 50% lowered the detectability of masses with borderline statistical significance. Dose reduction did not have a statistically significant effect on detection of microcalcifications. The model results indicate that there is room for optimization of dose level in mammographic screening procedures.

Classification images for simple detection and discrimination tasks in correlated noise.

Abstract: We use the classification image technique to investigate the effect of white noise and various correlated Gaussian noise textures (low-pass, high-pass, and band-pass) on observer performance in detection and discrimination tasks For these tasks, performance is generally enhanced by an observer's ability to "prewhiten" correlated noise as part of the formation of a decision variable We find that observer efficiency in these tasks is well represented by the measured classification images and that human observers show strong evidence of adaptation to different correlated noise textures This adaptation is captured in the frequency weighting of the classification images

The temporal dynamics of selective attention of the visual periphery as measured by classification images.

Abstract: This study estimates the temporal dynamics of selective attention with classification images, a technique assessing observer information use by tracking how responses are correlated with external noise added to the stimulus. Three observers performed a yes/no discrimination of a Gaussian signal that could appear at one of eight locations (eccentricityV4.6-). During the stimulus duration (300 ms), a peripheral cue indicated the potential signal location with 100% validity, and stimuli were presented in frames (37.5 ms/frame) of independently sampled Gaussian luminance image noise. Stimuli were presented either with or without a succeeding masking display (100 ms) of high-contrast image noise, with mask presence having little effect. The results from the classification images suggest that observers were able to use information at the cued location selectively (relative to the uncued locations), starting within the first (0–37.5 ms) or second (37.5–75 ms) frame. This suggests a selective attention effect earlier than those found in previous behavioral and eventrelated potential (ERP) studies, which generally have estimated the latency for selective attention effects to be 75–100 ms. We present a deconvolution method using the known temporal impulse response of early vision that indicates how the classification image results might relate to previous behavioral and ERP results. Applying the model to the classification images suggests that accounting for the known temporal dynamics could explain at least part of the difference in results between classification images and the previous studies.

A mathematical model approach toward combining information from multiple image projections of the same patient

Abstract: In this study, we used a mathematical observer model to combine information obtained from multiple angular projections of the same breast to determine the overall detection performance of a multi-projection breast imaging system in detectability of a simulated mass. 82 subjects participated in the study and 25 angular projections of each breast were acquired. Projections from a simulated 3 mm 3-D lesion were added to the projection images. The lesion was assumed to be embedded in the compressed breast at a distance of 3 cm from the detector. Hotelling observer with Laguerre-Gauss channels (LG CHO) was applied to each image. Detectability was analyzed in terms of ROC curves and the area under ROC curves (AUC). The critical question studied is how to best integrate the individual decision variables across multiple (correlated) views. Towards that end, three different methods were investigated. Specifically, 1) ROCs from different projections were simply averaged; 2) the test statistics from different projections were averaged; and 3) a Bayesian decision fusion rule was used. Finally, AUC of the combined ROC was used as a parameter to optimize the acquisition parameters to maximize the performance of the system. It was found that the Bayesian decision fusion technique performs better than the other two techniques and likely offers the best approximation of the diagnostic process. Furthermore, if the total dose level is held constant at 1/25th of dual-view mammographic screening dose, the highest detectability performance is observed when considering only two projections spread along an angular span of 11.4°.

Mass detection on real and synthetic mammograms: human observer templates and local statistics

Abstract: In this study we estimated human observer templates associated with the detection of a realistic mass signal superimposed on real and simulated but realistic synthetic mammographic backgrounds. Five trained naive observers participated in two-alternative forced-choice (2-AFC) experiments in which they were asked to detect a spherical mass signal extracted from a mammographic phantom. This signal was superimposed on statistically stationary clustered lumpy backgrounds (CLB) in one instance, and on nonstationary real mammographic backgrounds in another. Human observer linear templates were estimated using a genetic algorithm. An additional 2-AFC experiment was conducted with twin noise in order to determine which local statistical properties of the real backgrounds influenced the ability of the human observers to detect the signal. Results show that the estimated linear templates are not significantly different for stationary and nonstationary backgrounds. The estimated performance of the linear template compared with the human observer is within 5% in terms of percent correct (Pc) for the 2-AFC task. Detection efficiency is significantly higher on nonstationary real backgrounds than on globally stationary synthetic CLB. Using the twin-noise experiment and a new method to relate image features to observers trial to trial decisions, we found that the local statistical properties preventing or making the detection task easier were the standard deviation and three features derived from the neighborhood gray-tone difference matrix: coarseness, contrast and strength. These statistical features showed a dependency with the human performance only when they are estimated within an area sufficiently small around the searched location. These findings emphasize that nonstationary backgrounds need to be described by their local statistics and not by global ones like the noise Wiener spectrum.

TU-EE-A4-03: A Comparison of Anatomical Noise Properties Between Breast CT and Projection Breast Imaging

Abstract: Purpose: To compare the statistical properties of the anatomical noise present in breast images.Images from a dedicated breast CT scanner are compared with mammographic projection images.Method and Materials: A dedicated breast CT (bCT) scanner was used to image the breasts of volunteers and patients. Twenty‐five of the available 105 patient datasets were selected for analysis.Noise power spectra (NPS) calculations were performed on the datasets for the right breast of the 25 patients in order to examine the statistical nature of the anatomical background of the breast. The projection images acquired at 80 kVp during the cone beam breast CT acquisition were analyzed using NPS calculations. Approximately 50 projection images and 50 bCT slice images were analyzed per patient. Three regions of interest (ROIs ) were used per image. The ROIs were randomly distributed within the boundary of the breast. The 2D NPS was calculated for each ROI and the average 2D NPS was computed and averaged radially yielding a 1D NPS. A power law expression of the form αf−β was computed from the results of the projection and bCT images, and the β values were compared. Theoretical development suggests that the β for bCT should be one less than that of the projection images, i.e. β [bCT] = β [proj] − 1. Results: The β values for the bCT slice images were consistently less than those for the projection images. The difference ranged from 0.90 to 2.15, with the average difference being 1.34. The β values for projection images were consistent with the 2.8 value described by Burgess (2001). Conclusion: Lower β values from the NPS results of the bCT images could quantitatively indicate potential improvement in detection ability. More work is needed to conclusively say if this is the case.

A bivariate binormal ROC methodology for comparing new methods to an existing standard for screening applications

Abstract: Validating the use of new imaging technologies for screening large patient populations is an important and very challenging area of diagnostic imaging research. A particular concern in ROC studies evaluating screening technologies is the problem of verification bias, in which an independent verification of disease status is only available for a subpopulation of patients, typically those with positive results by a current screening standard. For example, in screening mammography, a study might evaluate a new approach using a sample of patients that have undergone needle biopsy following a standard mammogram and subsequent work-up. This case sampling approach provides accurate independent verification of ground truth and increases the prevalence of disease cases. However, the selection criteria will likely bias results of the study. In this work we present an initial exploration of an approach to correcting this bias within the parametric framework of binormal assumptions. We posit conditionally bivariate normal distributions on the latent decision variable for both the new methodology as well as the screening standard. In this case, verification bias can be seen as the effect of missing data from an operating point in the screening standard. We examine the magnitude of this bias in the setting of breast cancer screening with mammography, and we derive a maximum likelihood approach to estimating bias corrected ROC curves in this model.

P6C-5 Information Theoretic Design of Breast Sonography

Abstract: Assessing and optimizing diagnostic performance of medical imaging technology is important and challenging. We have extended an information theoretic approach based on the ideal observer for the discrimination of breast lesions in sonography to discover system configurations optimized for specific lesions features. The focus in this work is to examine the effects of various beamforming strategies on the discrimination of lesion boundary features.