Quantitative Analysis for Breast Density Estimation in Low Dose Chest CT Scans

Abstract

A computational method was developed for the measurement of breast density using chest computed tomography (CT) images and the correlation between that and mammographic density. Sixty-nine asymptomatic Asian women (138 breasts) were studied. With the marked lung area and pectoralis muscle line in a template slice, demons algorithm was applied to the consecutive CT slices for automatically generating the defined breast area. The breast area was then analyzed using fuzzy c-mean clustering to separate fibroglandular tissue from fat tissues. The fibroglandular clusters obtained from all CT slices were summed then divided by the summation of the total breast area to calculate the percent density for CT. The results were compared with the density estimated from mammographic images. For CT breast density, the coefficient of variations of intraoperator and interoperator measurement were 3.00 % (0.59 %-8.52 %) and 3.09 % (0.20 %-6.98 %), respectively. Breast density measured from CT (22 ± 0.6 %) was lower than that of mammography (34 ± 1.9 %) with Pearson correlation coefficient of r=0.88. The results suggested that breast density measured from chest CT images correlated well with that from mammography. Reproducible 3D information on breast density can be obtained with the proposed CT-based quantification methods.

Opportunistic Breast Density Assessment in Women Receiving Low-dose Chest Computed Tomography Screening

“…After removing the thoracic cavity region, the bilateral breast regions, including the chest wall, can be obtained ( Figure 2d ). The obtained breast region on this slice was then used as the template for the adjacent superior and inferior slices, following the procedure developed for segmenting breast region on MRI (16), and the process continued until reaching the beginning and ending slices. On a case-by-case basis, the operator may need to do some fine adjustments to cover the entire breast without cutting out any fibroglandular tissue.…”

“…On a case-by-case basis, the operator may need to do some fine adjustments to cover the entire breast without cutting out any fibroglandular tissue. To further remove the chest wall, we used several morphological methods and fuzzy-C-means (FCM) segmentation algorithm to obtain the edge of the chest wall region, following the methods developed for removing chest wall muscle on MRI (detailed procedures were described in (16)).…”

“…Robust segmentation algorithms thus are required. A quantitative density segmentation method for LDCT was reported recently, showing that breast density measured from LDCT was lower than mammographic density (22±0.6 % vs. 34±1.9 %), but with a high Pearson correlation coefficient of r=0.88 (16). …”

Lung segmentation on standard and mobile chest radiographs using oriented Gaussian derivatives filter

“…Many researchers have tried to modify the basic objective function to have more robust FCM [18-23]. However, the ideal segmentation of an image is usually application-dependent; and FCM has been used with some success in the soft or fuzzy segmentation in medical imaging of chest CT [7,23-28], chest MRI [18] and brain MRI [16,19-22,25,29]. In CT and MRI images, the edges of the lung or brain can be easily distinguished due to the distinct bone and cell tissue, thus motivated the authors to apply the FCM in their work.…”

“…The FCM method that was improved by [38-40] is used for this purpose because of its ability to automatically cluster the pixels into the defined number of clusters. FCM has been used previously in segmenting lung in chest X-Ray [15,16], chest CT [7,23-28], chest MRI [18] as well as segmenting brain matter in MRI images [16,19-22,25,29]. …”