Biomedical subjects

C K Abbey

Publications and source records attributed to C K Abbey.

12 recordsLinked to original sources

PubMed2002

A porcine coronary stent model of increased neointima formation in the left anterior descending coronary artery.

BACKGROUND: Clinical trials suggest an increased frequency of restenosis after coronary intervention in left anterior descending (LAD) compared to the left circumflex or right coronary arteries. Experimental studies correlate stent-induced arterial injury and the extent of neointima formation. This study investigates whether the coronary artery affects the relationship between arterial injury and neointima hyperplasia in the porcine stent model. METHODS: Non-lipemic farm pigs underwent stent placement in the LAD (n = 26) and the right coronary artery (RCA; n = 30). Quantitative coronary angiography (QCA) was performed before and after stent placement, and at follow-up; quantitative histomorphometry and injury score were analyzed at 30-day follow-up. RESULTS: Initial procedure balloon/artery ratios (LAD 1.17 +/- 0.11 vs RCA 1.17 +/- 0.09, P = NS), and minimal stent lumen diameters (MLD; LAD 2.91 +/- 0.31 vs RCA: 2.93 +/- 0.28 mm, P = NS) were similar suggesting no difference in deployment technique. At follow-up there was more restenosis in the LAD (diameter stenosis: 55.0 +/- 26.4% vs 37.3 +/- 18.1%, and MLD: 1.24 +/- 0.78 mm vs. 1.71 +/- 0.57 mm, P < 0.05 for both comparisons). No differences were seen for injury score (1.09 +/- 0.51 vs 1.01 +/- 0.57; LAD vs RCA) or stent area (6.13 +/- 0.99 vs 6.55 +/- 1.42 mm2). Histomorphometry demonstrated smaller lumen area (2.15 +/- 0.94 vs 2.96 +/- 1.29 mm2) and thicker neointima (0.63 +/- 0.25 vs 0.51 +/- 0.17 mm; all P < 0.05) in the LAD. Multiple linear regression analysis identified the LAD as an independent predictive factor for increased neointima formation. CONCLUSIONS: These observations establish an animal model that is consistent with clinical experience showing that restenosis after stenting is more common in the LAD. The findings may be useful for understanding and developing systemic and local antirestenotic strategies.

Angioplasty, Balloon, Coronary↗

PubMed2001

Accuracy assessment of layer decomposition using simulated angiographic image sequences.

Layer decomposition is a promising method for obtaining accurate densitometric profiles of diseased coronary artery segments. This method decomposes coronary angiographic image sequences into moving densitometric layers undergoing translation, rotation, and scaling. In order to evaluate the accuracy of this technique, we have developed a technique for embedding realistic simulated moving stenotic arteries in real clinical coronary angiograms. We evaluate the accuracy of layer decomposition in two ways. First, we compute tracking errors as the distance between the true and estimated motion of a reference point in the arterial lesion. We find that noise-weighted phase correlation and layered background subtraction are superior to cross correlation and fixed mask subtraction, respectively. Second, we compute the correlation coefficient between the true vessel profile and the raw and processed images in the region of the stenosis. We find that layer decomposition significantly improves the correlation coefficient.

Algorithms↗

PubMed2001

Computerized classification of suspicious regions in chest radiographs using subregion Hotelling observers.

We propose to investigate the use of subregion Hotelling observers (SRHOs) in conjunction with perceptrons for the computerized classification of suspicious regions in chest radiographs for being nodules requiring follow up. Previously, 239 regions of interest (ROIs), each containing a suspicious lesion with proven classification, were collected. We chose to investigate the use of SRHOs as part of a multilayer classifier to determine the presence of a nodule. Each SRHO incorporates information about signal, background, and noise correlation for classification. For this study, 225 separate Hotelling observers were set up in a grid across each ROI. Each separate observer discriminates an 8 by 8 pixel area. A round robin sampling scheme was used to generate the 225 features, where each feature is the output of the individual observers. These features were then rank ordered by the magnitude of the weights of a perceptron. Once rank ordered, subsets of increasing number of features were selected to be used in another perceptron. This perceptron was trained to minimize mean squared error and the output was a continuous variable representing the likelihood of the region being a nodule. Performance was evaluated by receiver operating characteristic (ROC) analysis and reported as the area under the curve (Az). The classifier was optimized by adding additional features until the Az declined. The optimized subset of observers then were combined using a third perceptron. A subset of 80 features was selected which gave an Az of 0.972. Additionally, at 98.6% sensitivity, the classifier had a specificity of 71.3% and increased the positive predictive value from 60.7% to 84.1 %. Preliminary results suggest that using SRHOs in combination with perceptrons can provide a successful classification scheme for pulmonary nodules. This approach could be incorporated into a larger computer aided detection system for decreasing false positives.

Algorithms↗

PubMed2001

Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability.

We consider detection of a nodule signal profile in noisy images meant to roughly simulate the statistical properties of tomographic image reconstructions in nuclear medicine. The images have two sources of variability arising from quantum noise from the imaging process and anatomical variability in the ensemble of objects being imaged. Both of these sources of variability are simulated by a stationary Gaussian random process. Sample images from this process are generated by filtering white-noise images. Human-observer performance in several signal-known-exactly detection tasks is evaluated through psychophysical studies by using the two-alternative forced-choice method. The tasks considered investigate parameters of the images that influence both the signal profile and pixel-to-pixel correlations in the images. The effect of low-pass filtering is investigated as an approximation to regularization implemented by image-reconstruction algorithms. The relative magnitudes of the quantum and the anatomical variability are investigated as an approximation to the effects of exposure time. Finally, we study the effect of the anatomical correlations in the form of an anatomical slope as an approximation to the effects of different tissue types. Human-observer performance is compared with the performance of a number of model observers computed directly from the ensemble statistics of the images used in the experiments for the purpose of finding predictive models. The model observers investigated include a number of nonprewhitening observers, the Hotelling observer (which is equivalent to the ideal observer for these studies), and six implementations of channelized-Hotelling observers. The human observers demonstrate large effects across the experimental parameters investigated. In the regularization study, performance exhibits a mild peak at intermediate levels of regularization before degrading at higher levels. The exposure-time study shows that human observers are able to detect ever more subtle lesions at increased exposure times. The anatomical slope study shows that human-observer performance degrades as anatomical variability extends into higher spatial frequencies. Of the observers tested, the channelized-Hotelling observers best capture the features of the human data.

Artifacts↗

PubMed2000

Simulating coronary arteries in x-ray angiograms.

Clinical validation of quantitative coronary angiography (QCA) algorithms is difficult due to the lack of a simple alternative method for accurately measuring in vivo vessel dimensions. We address this problem by embedding simulated coronary artery segments with known geometry in clinical angiograms. Our vessel model accounts for the profile of the vessel, x-ray attenuation in the original background, and noise in the imaging system. We have compared diameter measurements of our computer simulated arteries with measurements of an x-ray Telescopic-Shaped Phantom (XTSP) with the same diameters. The results show that for both uniform and anthropomorphic backgrounds there is good agreement in the measured diameters of XTSP compared to the simulated arteries (Pearson's correlation coefficient 0.99). In addition, the difference in accuracy and precision of the true diameter measures compared to the XTSP and simulated artery diameters was small (mean absolute error across all diameters was < or = 0.11 mm +/- 0.09 mm).

Algorithms↗

PubMed2000

Visual signal detection in structured backgrounds. III. Calculation of figures of merit for model observers in statistically nonstationary backgrounds.

Models of human visual detection have been successfully used in computer-generated noise. For these backgrounds, which are generally statistically stationary, model performance can be readily calculated by computing the index of detectability d' from the noise power spectrum, the signal profile, and the model template. However, model observers are ultimately needed in more real backgrounds, which may be statistically non-stationary. We investigated different methods to calculate figures of merit for model observers in real backgrounds based on different assumptions about image stationarity. We computed performance of the nonpre-whitening matched-filter observer with an eye filter on mammography and coronary angiography for an additive or a multiplicative signal. Performance was measured either by applying the model template to the images or by computing closed-form expressions with various assumptions about image stationarity. Results show first that the structured backgrounds investigated cannot be considered stationary. Second, traditional closed-form expressions of detectability calculated from the noise power spectra with the assumption of background stationarity lead to erroneous estimates of model performance. Third, the most accurate way of measuring model performances is by directly applying the model template on the images or by computing a closed-form expression that does not assume image stationarity.

Choice Behavior↗

PubMed2000

Visual signal detection in structured backgrounds. IV. Figures of merit for model performance in multiple-alternative forced-choice detection tasks with correlated responses.

Many investigators are currently developing models to predict human performance in detecting a signal embedded in complex backgrounds. A common figure of merit for model performance is d', an index of detectability that can be mathematically related to the proportion correct (Pc) when the responses of the model are Gaussian distributed and statistically independent. However, in many multiple-alternative forced-choice (MAFC) detection tasks, the target appears in one of M different locations within an image. If the image contains slow spatially varying luminance changes (low-pass noise), the pixel luminance values at the possible signal locations are correlated and therefore the model/human responses to the different locations might also be correlated. We investigate the effect of response correlations on model performance and compare different figures of merit for these conditions. Our results show that use of the standard d' index of detectability assuming statistical independence can lead to erroneous underestimates of Pc and misleading comparisons of models. We introduce a novel figure of merit d'(r) that takes into account response correlations and can be used to accurately estimate Pc. Furthermore, we show that d'(r) can be readily related to the standard index of detectability d' by d'(r) = d'/square root of (1 - r), where r is the correlation between the responses in any MAFC detection task. We illustrate the use of the theory by computing figures of merit for two linear models detecting a signal in one of four locations within medical image backgrounds.

Choice Behavior↗

PubMed2000

Derivation of a detectability index for correlated responses in multiple-alternative forced-choice experiments.

In a recent paper [J. Opt. Soc. Am. A 17, 206 (2000)], a modified detectability index, d'r, was proposed to accommodate correlations between internal responses in multiple-alternative forced-choice (MAFC) experiments. The derivation given in that work pertained only to two-alternative forced choice, although it was shown empirically that the result held for general MAFC tasks when the correlation between responses is constant. Here we present a rigorous derivation that shows that the d'r result generalizes to MAFC tasks in this case.

Choice Behavior↗

PubMed1998

A method for approximating the density of maximum-likelihood and maximum a posteriori estimates under a Gaussian noise model.

The performance of maximum-likelihood (ML) and maximum a posteriori (MAP) estimates in non-linear problems at low data SNR is not well predicted by the Cramér-Rao or other lower bounds on variance. In order to better characterize the distribution of ML and MAP estimates under these conditions, we derive a point approximation to density values of the conditional distribution of such estimates. In an example problem, this approximate distribution captures the essential features of the distribution of ML estimates in the presence of Gaussian-distributed noise.

Algorithms↗

PubMed1998

Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood-generating functions.

We continue the theme of previous papers [J. Opt. Soc. Am. A 7, 1266 (1990); 12, 834 (1995)] on objective (task-based) assessment of image quality. We concentrate on signal-detection tasks and figures of merit related to the ROC (receiver operating characteristic) curve. Many different expressions for the area under an ROC curve (AUC) are derived for an arbitrary discriminant function, with different assumptions on what information about the discriminant function is available. In particular, it is shown that AUC can be expressed by a principal-value integral that involves the characteristic functions of the discriminant. Then the discussion is specialized to the ideal observer, defined as one who uses the likelihood ratio (or some monotonic transformation of it, such as its logarithm) as the discriminant function. The properties of the ideal observer are examined from first principles. Several strong constraints on the moments of the likelihood ratio or the log likelihood are derived, and it is shown that the probability density functions for these test statistics are intimately related. In particular, some surprising results are presented for the case in which the log likelihood is normally distributed under one hypothesis. To unify these considerations, a new quantity called the likelihood-generating function is defined. It is shown that all moments of both the likelihood and the log likelihood under both hypotheses can be derived from this one function. Moreover, the AUC can be expressed, to an excellent approximation, in terms of the likelihood-generating function evaluated at the origin. This expression is the leading term in an asymptotic expansion of the AUC; it is exact whenever the likelihood-generating function behaves linearly near the origin. It is also shown that the likelihood-generating function at the origin sets a lower bound on the AUC in all cases.

Area Under Curve↗

PubMed1997

Visual signal detectability with two noise components: anomalous masking effects.

We measured human observers' detectability of aperiodic signals in noise with two components (white and low-pass Gaussian). The white-noise component ensured that the signal detection task was always noise limited rather than contrast limited (i.e., image noise was always much larger than observer internal noise). The low-pass component can be considered to be a statistically defined background. Contrast threshold elevation was not linearly related to the rms background contrast. Our results gave power-law exponents near 0.6, similar to that found for deterministic masking. The Fisher-Hotelling linear discriminant model assessed by Rolland and Barrett [J. Opt. Soc. Am. A 9, 649 (1992)] and the modified nonprewhitening matched filter model suggested by Burgess [J. Opt. Soc. Am. A 11, 1237 (1994)] for describing signal detection in statistically defined backgrounds did not fit our more precise data. We show that it is not possible to find any nonprewhitening model that can fit our data. We investigated modified Fisher-Hotelling models by using spatial-frequency channels, as suggested by Myers and Barrett [J. Opt. Soc. Am. A 4, 2447 (1987)]. Two of these models did give good fits to our data, which suggests that we may be able to do partial prewhitening of image noise.

Contrast Sensitivity↗

PubMedDate not supplied

Toward fully automated high-resolution electron tomography.

Electron tomography is a powerful tool in elucidating the three-dimensional architecture of large biological complexes and subcellular organelles. Its use can be expanded through the simplification of the tomographic procedure by automation of its tasks. In this paper, we describe our EMACT/EMCAT system, which automates both tomographic data collection and reconstruction.

Animals↗