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Region of interest based Hotelling observer for computed tomography with comparison to alternative methods.

Sanchez AA, Sidky EY, Pan X - J Med Imaging (Bellingham) (2014)

Bottom Line: This reduces the dimensionality of the image covariance matrix so that direct computation of HO metrics within the ROI is feasible.Here, we compare several of these methods, including the use of Laguerre-Gauss channels, discrete Fourier domain computation of the HO (which assumes noise stationarity), and two approaches to HO estimation through samples of noisy images.Since our method computes HO performance exactly within an ROI, this allows us to investigate the validity of the assumptions inherent in various common approaches to HO estimation, such as the stationarity assumption in the case of the discrete Fourier transform domain method.

View Article: PubMed Central - PubMed

Affiliation: The University of Chicago, Department of Radiology, 5841 South Maryland Avenue, Chicago, Illinois 60615, United States.

ABSTRACT

We compare several approaches to estimation of Hotelling observer (HO) performance in x-ray computed tomography (CT). We consider the case where the signal of interest is small so that the reconstructed image can be restricted to a small region of interest (ROI) surrounding the signal. This reduces the dimensionality of the image covariance matrix so that direct computation of HO metrics within the ROI is feasible. We propose that this approach is directly applicable to systems optimization in CT; however, many alternative approaches exist, which make computation of HO performance tractable through a range of approximations, assumptions, or estimation strategies. Here, we compare several of these methods, including the use of Laguerre-Gauss channels, discrete Fourier domain computation of the HO (which assumes noise stationarity), and two approaches to HO estimation through samples of noisy images. Since our method computes HO performance exactly within an ROI, this allows us to investigate the validity of the assumptions inherent in various common approaches to HO estimation, such as the stationarity assumption in the case of the discrete Fourier transform domain method.

No MeSH data available.


Shown here are the results from Fig. 9 for the largest number of sample images with error bars denoting 95% confidence intervals derived from 1000 bootstrap samples. These errors derive from the variance of the training and testing estimator, while inherent bias in the estimator contributes additional error, especially for small filter widths where the number of ROI pixels is large. As in Fig. 8, the error bars here only convey statistical uncertainties, and bias can be inferred by comparison with the analytically computed ROI-HO curves. The results in (a) correspond to the hold-out approach, while the results in (b) correspond to resubstitution.
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f10: Shown here are the results from Fig. 9 for the largest number of sample images with error bars denoting 95% confidence intervals derived from 1000 bootstrap samples. These errors derive from the variance of the training and testing estimator, while inherent bias in the estimator contributes additional error, especially for small filter widths where the number of ROI pixels is large. As in Fig. 8, the error bars here only convey statistical uncertainties, and bias can be inferred by comparison with the analytically computed ROI-HO curves. The results in (a) correspond to the hold-out approach, while the results in (b) correspond to resubstitution.

Mentions: (a) HO estimates from training and testing performed using the hold-out approach for 500, 1000, and 1500 training images, with an equal number of testing images. The prevalence of images from each class is also equal. (b) HO estimates resulting from training and testing performed using resubstitution for 1000, 2000, and 3000 total images. The bias and variance of the estimates is worst for narrow filter widths, where the size of the ROI used is largest. Variance of the estimates is illustrated in Fig. 10 through 95% confidence intervals derived from bootstraping.


Region of interest based Hotelling observer for computed tomography with comparison to alternative methods.

Sanchez AA, Sidky EY, Pan X - J Med Imaging (Bellingham) (2014)

Shown here are the results from Fig. 9 for the largest number of sample images with error bars denoting 95% confidence intervals derived from 1000 bootstrap samples. These errors derive from the variance of the training and testing estimator, while inherent bias in the estimator contributes additional error, especially for small filter widths where the number of ROI pixels is large. As in Fig. 8, the error bars here only convey statistical uncertainties, and bias can be inferred by comparison with the analytically computed ROI-HO curves. The results in (a) correspond to the hold-out approach, while the results in (b) correspond to resubstitution.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4326074&req=5

f10: Shown here are the results from Fig. 9 for the largest number of sample images with error bars denoting 95% confidence intervals derived from 1000 bootstrap samples. These errors derive from the variance of the training and testing estimator, while inherent bias in the estimator contributes additional error, especially for small filter widths where the number of ROI pixels is large. As in Fig. 8, the error bars here only convey statistical uncertainties, and bias can be inferred by comparison with the analytically computed ROI-HO curves. The results in (a) correspond to the hold-out approach, while the results in (b) correspond to resubstitution.
Mentions: (a) HO estimates from training and testing performed using the hold-out approach for 500, 1000, and 1500 training images, with an equal number of testing images. The prevalence of images from each class is also equal. (b) HO estimates resulting from training and testing performed using resubstitution for 1000, 2000, and 3000 total images. The bias and variance of the estimates is worst for narrow filter widths, where the size of the ROI used is largest. Variance of the estimates is illustrated in Fig. 10 through 95% confidence intervals derived from bootstraping.

Bottom Line: This reduces the dimensionality of the image covariance matrix so that direct computation of HO metrics within the ROI is feasible.Here, we compare several of these methods, including the use of Laguerre-Gauss channels, discrete Fourier domain computation of the HO (which assumes noise stationarity), and two approaches to HO estimation through samples of noisy images.Since our method computes HO performance exactly within an ROI, this allows us to investigate the validity of the assumptions inherent in various common approaches to HO estimation, such as the stationarity assumption in the case of the discrete Fourier transform domain method.

View Article: PubMed Central - PubMed

Affiliation: The University of Chicago, Department of Radiology, 5841 South Maryland Avenue, Chicago, Illinois 60615, United States.

ABSTRACT

We compare several approaches to estimation of Hotelling observer (HO) performance in x-ray computed tomography (CT). We consider the case where the signal of interest is small so that the reconstructed image can be restricted to a small region of interest (ROI) surrounding the signal. This reduces the dimensionality of the image covariance matrix so that direct computation of HO metrics within the ROI is feasible. We propose that this approach is directly applicable to systems optimization in CT; however, many alternative approaches exist, which make computation of HO performance tractable through a range of approximations, assumptions, or estimation strategies. Here, we compare several of these methods, including the use of Laguerre-Gauss channels, discrete Fourier domain computation of the HO (which assumes noise stationarity), and two approaches to HO estimation through samples of noisy images. Since our method computes HO performance exactly within an ROI, this allows us to investigate the validity of the assumptions inherent in various common approaches to HO estimation, such as the stationarity assumption in the case of the discrete Fourier transform domain method.

No MeSH data available.