Rationale and Objectives
The purpose of this study was to compare the precision of mammographic breast density measurement using radiologist reader assessment, histogram threshold segmentation, fuzzy C-mean segmentation, and spectral material decomposition.
Materials and Methods
Spectral mammography images from a total of 92 consecutive asymptomatic women (aged 50–69 years) who presented for annual screening mammography were retrospectively analyzed for this study. Breast density was estimated using 10 radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and spectral material decomposition. The breast density correlation between left and right breasts was used to assess the precision of these techniques to measure breast composition relative to dual-energy material decomposition.
Results
In comparison to the other techniques, the results of breast density measurements using dual-energy material decomposition showed the highest correlation. The relative standard error of estimate for breast density measurements from left and right breasts using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and dual-energy material decomposition was calculated to be 1.95, 2.87, 2.07, and 1.00, respectively.
Conclusions
The results indicate that the precision of dual-energy material decomposition was approximately factor of two higher than the other techniques with regard to better correlation of breast density measurements from right and left breasts.
Mammographic breast density is an important risk factor in the development of breast cancer . Previous reports have shown that women with the highest mammographic density (75%–100%) have 4- to 5-fold increased risk of developing breast cancer compared to the lowest density (0%–25%) . Furthermore, it has been shown that the sensitivity of screening mammography is lower among women with dense breasts . Therefore, improved methods of measuring breast density could potentially be helpful in more accurately quantifying breast cancer risk and monitor changes in risk over time. This is especially important because breast density can change with external factors such as hormonal agents and diet. The importance of quantitative breast density assessment has been highlighted by a previous report indicating that for every 1% increase of mammographic breast density, there is a 2% increase of the relative risk for breast cancer .
Qualitative classification of mammographic breast density is the current clinical standard. However, subjective classification of breast density is limited by its considerable intra reader and inter reader variability . Therefore, there have been previous reports of more automated methods using area-based and volume-based techniques to measure breast density . The area-based techniques essentially use a histogram of image gray levels for segmentation of fibroglandular and adipose tissues . These techniques are limited by the segmentation process and the fact that the three-dimensional nature of the breast is not taken into account. The current volume-based techniques use paddle position and a shape model for estimation of breast thickness, which is used in breast thickness calculation . However, these techniques are limited by the assumptions required in the breast shape model and the errors associated with the paddle position measurement, which can lead to a 2-to 3-fold increase in measurement error in volumetric breast density .
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Materials and methods
Image Acquisition
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Breast Density Measurement
Radiologist Reader Assessment
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Histogram Threshold Segmentation
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Fuzzy C-Mean Segmentation
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Dual-Energy Material Decomposition
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Statistical Analysis
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Results
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Table 1
Summary of the Linear Regression Analysis Between Left and Right Breast Density Measurements for Various Methods
Method Slope Intercept, % Pearson r Normalized Relative Variance Readers 0.90 8.1 0.93 1.95 Cumulus 0.87 5.0 0.80 2.87 Fuzzy C-mean 0.79 4.3 0.79 2.07 Spectral 0.90 1.1 0.96 1.00
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Discussion
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Acknowledgments
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