Three MRI images acquired using different pulse sequences, (a) a nonfat-sat T1 weighted image using gradient echo pulse sequence on a 1.5 T Philips scanner, (b) a fat-sat T1 weighted image using the spectral attenuated inversion recovery fat suppression gradient echo sequence on a Philips scanner, and (c) a fat-sat T1 weighted image using short TI inversion recovery fat suppression gradient echo sequence on a GE scanner. All images show clear contrast between fibroglandular tissue and fatty tissue, and the skin shows a similar intensity as the fibroglandular tissue. Depending on the methods used for fat suppression, the skin may show different intensities, very bright in (b), and much darker in (a) and (c). (d) An example of mammographic image. Due to the projection nature of mammography, the x-ray beam is equally attenuated by two layers of skin everywhere within the field of view of the breast, and thus the skin effect is uniformly present on the entire image and can be ignored during mammographic density measurement.
Fibroglandular tissue segmentation results of three examples with different breast densities. Case I: Fatty breast; case II: Moderately dense breast; and case III: Dense breast. (a) The segmented breast. (b) The FCM clustering segmentation results without skin exclusion. The skin and some fibroglandular tissues are categorized into the same cluster. The tangential line at each location along the breast-air boundary curve is determined first (white arrows), and then the dynamic search is performed along the perpendicular direction (black arrows) from the air to the skin to the breast. (c) The segmentation results of the skin. (d) 3D rendering view of the segmented skin. (e) 3D rendering results of the segmented skin with fibroglandular tissue inside. These three cases have comparable breast volumes (I: , II: , and III: ) and skin volumes (I: , II: , and III: ).
Comparison between the percent densities measured with and without skin exclusion from all 50 analyzed cases, demonstrating the overestimated density without skin exclusion. A strong correlation is found. The best fitting equation is (, ), where is the percent density with skin (i.e., without skin exclusion), and is the percent density without skin (i.e., with skin exclusion). The slope is 1.23; therefore, the difference cannot be accounted for by using the intercept as an offset value.
Correlation between the estimated skin-excluded percent density and the measured percent density with skin exclusion using two models. (a) The skin volume was estimated from the breast volume using . When using the breast volume model to estimate the skin, the estimated percent density falls on the unity line with the measured percent density with skin exclusion, indicating a high agreement between these two sets of data. The fitting yields adjusted and a root mean square , . (b) The skin volume was estimated as a fixed value of 8%. Although the data points are still close to the unity line, but the deviation from the line is apparently larger compared to (a). The fitting yields a lower adjusted , () and a higher root mean square , .
The measured breast volume, fibroglandular tissue volume, and skin volume of all 50 analyzed cases.
The measured percent densities with and without skin exclusion of the three example cases shown in Fig. 2.
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