Classification of breast computed tomography data

Nelson, Thomas R.; Cerviño, Laura; Boone, John M.; Lindfors, Karen K.

doi:10.1118/1.2839439

Cited by 62 publications

(69 citation statements)

References 43 publications

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“…We note that the average density, approximately 30%, is consistent with other investigations of bCT. 31 It is clear that the bCT exponents increase as a function of the glandular fraction. The Pearson correlation between this measure of breast density and the power-law exponent is 0.81 for bCT images and 0.74 for segmented bCT images.…”

Section: Ivb Dependence On Breast Densitymentioning

confidence: 99%

“…36 Another example is that the bCT images tend to become more glandular as the CT slice approaches the nipple region. 31 However, the use of randomly chosen subregions masks this issue to some extent since the ROIs lose their gross reference to the breast, and thereby induce more stationarity than is likely present in the breast itself. A second limitation is the use of a second order statistic-such as the PS-to characterize anatomical variability.…”

Section: Vc Limitations Of the Studymentioning

confidence: 99%

“…30,31 The algorithm first segments out the breast from the background using a histogram based two-means clustering algorithm. Two con- secutive iterative processes are then applied to segment glandular tissue from adipose tissue, both using percent glandularity as a convergence test.…”

Section: Iiic Breast Ct Segmentationsmentioning

confidence: 99%

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Characterizing anatomical variability in breast CT images

et al. 2008

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Previous work ͓Burgess et al., Med. Phys. 28, 419-437 ͑2001͔͒ has shown that anatomical noise in projection mammography results in a power spectrum well modeled over a range of frequencies by a power law, and the exponent ͑␤͒ of this power law plays a critical role in determining the size at which a growing lesion reaches the threshold for detection. In this study, the authors evaluated the power-law model for breast computed tomography ͑bCT͒ images, which can be thought of as thin sections through a three-dimensional ͑3D͒ volume. Under the assumption of a 3D power law describing the distribution of attenuation coefficients in the breast parenchyma, the authors derived the relationship between the power-law exponents of bCT and projection images and found it to be ␤ section = ␤ proj − 1. They evaluated this relationship on clinical images by comparing bCT images from a set of 43 patients to Burgess' findings in mammography. They were able to make a direct comparison for 6 of these patients who had both a bCT exam and a digitized film-screen mammogram. They also evaluated segmented bCT images to investigate the extent to which the bCT power-law exponent can be explained by a binary model of attenuation coefficients based on the different attenuation of glandular and adipose tissue. The power-law model was found to be a good fit for bCT data over frequencies from 0.07 to 0.45 cyc/ mm, where anatomical variability dominates the spectrum. The average exponent for bCT images was 1.86. This value is close to the theoretical prediction using Burgess' published data for projection mammography and for the limited set of mammography data available from the authors' patient sample. Exponents from the segmented bCT images ͑average value: 2.06͒ were systematically slightly higher than bCT images, with substantial correlation between the two ͑r = 0.84͒.

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Section: Ivb Dependence On Breast Densitymentioning

confidence: 99%

Section: Vc Limitations Of the Studymentioning

confidence: 99%

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Characterizing anatomical variability in breast CT images

et al. 2008

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“…After bias field correction, the breast densities of the 40 postmortem breasts ranged from approximately 8% to 57%, with an average value of 27.6%, which agrees well with the most recent estimates of clinical breast density. 36 It should be noted though that these numbers are volumetric measures of the breast density, which have a much smaller range than area-based measures such as the BIRADS classification of mammographic breast density. 37 We have also performed cone beam CT scans on the same postmortem breast samples to determine the breast density from the reconstructed 3D CT images.…”

Section: Discussionmentioning

confidence: 99%

Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: A postmortem study

et al. 2013

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Purpose: Quantification of breast density based on three-dimensional breast MRI may provide useful information for the early detection of breast cancer. However, the field inhomogeneity can severely challenge the computerized image segmentation process. In this work, the effect of the bias field in breast density quantification has been investigated with a postmortem study. Methods: T1-weighted images of 20 pairs of postmortem breasts were acquired on a 1.5 T breast MRI scanner. Two computer-assisted algorithms were used to quantify the volumetric breast density. First, standard fuzzy c-means (FCM) clustering was used on raw images with the bias field present. Then, the coherent local intensity clustering (CLIC) method estimated and corrected the bias field during the iterative tissue segmentation process. Finally, FCM clustering was performed on the bias-fieldcorrected images produced by CLIC method. The left-right correlation for breasts in the same pair was studied for both segmentation algorithms to evaluate the precision of the tissue classification. Finally, the breast densities measured with the three methods were compared to the gold standard tissue compositions obtained from chemical analysis. The linear correlation coefficient, Pearson's r, was used to evaluate the two image segmentation algorithms and the effect of bias field. Results: The CLIC method successfully corrected the intensity inhomogeneity induced by the bias field. In left-right comparisons, the CLIC method significantly improved the slope and the correlation coefficient of the linear fitting for the glandular volume estimation. The left-right breast density correlation was also increased from 0.93 to 0.98. When compared with the percent fibroglandular volume (%FGV) from chemical analysis, results after bias field correction from both the CLIC the FCM algorithms showed improved linear correlation. As a result, the Pearson's r increased from 0.86 to 0.92 with the bias field correction. Conclusions:The investigated CLIC method significantly increased the precision and accuracy of breast density quantification using breast MRI images by effectively correcting the bias field. It is expected that a fully automated computerized algorithm for breast density quantification may have great potential in clinical MRI applications.

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“…For early detection of breast cancer, the visibility of breast masses in the mammogram is very important. However, the superposition of glandular structures causes relatively low contrast, which limits detectability of cancer [1]. To enhance detectability of breast cancer, dual-energy contrast-enhanced digital mammography (CEDM) has been used as a technique with potential to improve the detection of breast carcinomas, which involves intravenous injections of iodinated contrast media, in conjunction with mammography examination [2,3].…”

Section: Introductionmentioning

confidence: 99%

A Quantification Method for Breast Tissue Thickness and Iodine Concentration Using Photon-Counting Detector

Han

2015

J Digit Imaging

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The purpose of contrast-enhanced digital mammography (CEDM) is to facilitate detection and characterization of the lesions in the breast using intravenous injection of an iodinated contrast agent. CEDM produces iodine images with gray levels proportional to iodine concentration at each pixel, which can be considered as quantification of iodine. While dual-energy CEDM requires an accurate knowledge of the thickness of compressed breast for the quantification, it is known that the accuracy of the built-in thickness measurement is not satisfactory. Triple-energy CEDM, which can provide a third image, can alleviate the limitation of dual-energy CEDM. If triple exposure technique is applied, it can lead to increased risk of motion artifact. An energy-resolving photoncounting detector (PCD) that can acquire multispectral X-ray images can reduce the risk of motion artifact. In this research, an easily implementable method for iodine quantification in breast imaging was suggested, and it was applied to the images of breast phantom with various iodine concentrations. The iodine concentrations in breast phantom simulate lesions filled with different iodine concentrations in the breast. The result shows that the proposed method can quantify the iodine concentrations in breast phantom accurately.

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Classification of breast computed tomography data

Cited by 62 publications

References 43 publications

Characterizing anatomical variability in breast CT images

Characterizing anatomical variability in breast CT images

Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: A postmortem study

A Quantification Method for Breast Tissue Thickness and Iodine Concentration Using Photon-Counting Detector

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