Cone Beam Breast CT is a three-dimensional breast imaging modality with high contrast resolution and no tissue overlap.
With these advantages, it is possible to measure volumetric breast density accurately and quantitatively with CBBCT 3D
images. Three major breast components need to be segmented: skin, fat and glandular tissue. In this research, a modified
morphological processing is applied to the CBBCT images to detect and remove the skin of the breast. After the skin is
removed, a 2-step fuzzy clustering scheme is applied to the CBBCT image volume to adaptively cluster the image voxels
into fat and glandular tissue areas based on the intensity of each voxel. Finally, the CBBCT breast volume images are
divided into three categories: skin, fat and glands. Clinical data is used and the quantitative CBBCT breast density
evaluation results are compared with the mammogram-based BIRADS breast density categories.
In Cone Beam Breast CT (CBBCT), breast calcifications have higher intensities than the surrounding tissues. Without
the superposition of breast structures, the three-dimensional distribution of the calcifications can be revealed. In this
research, based on the fact that calcifications have higher contrast, a local thresholding and a histogram thresholding
were used to select candidate calcification areas. Six features were extracted from each candidate calcification: average
foreground CT number value, foreground CT number standard deviation, average background CT number value,
background CT number standard deviation, foreground-background contrast, and average edge gradient. To reduce the
false positive candidate calcifications, a feed-forward back propagation artificial neural network was designed. The
artificial neural network was trained with the radiologists confirmed calcifications and used as classifier in the
calcification auto-detection task. In the preliminary experiments, 90% of the calcifications in the testing data sets were
detected correctly with an average of 10 false positives per data set.
Flat-panel detector-based cone beam CT usually employs FDK algorithm as the reconstruction method. Traditionally, the
row-wise ramp linear filtering was regularized by noise-suppression windows, such as Shepp-Logan, Hamming windows
etc before the backprojection to get the final acceptable (in terms of SNR) reconstructed 3-D volume data. Though noise
was reduced, this linear filtering regularized by noise suppression window had the potential to affect the signal spatial
resolution and thus to reduce the sharpness of the structure boundaries within the breast image especially impeding the
detection of the small calcifications and very small abnormalities that may indicate early breast cancer. Furthermore, the
reconstructed images were still characterized by smudges. In order to combat the aforementioned shortcomings, a Wavelet regularization method was conducted on projection data followed by row-wise ramp linear filtering inherited
Cone Beam Breast CT (CBBCT) acquires 3D breast images without compression to the breast. More detailed and
accurate information of breast lesions is revealed in CBBCT images. In our research, based on the observation that tumor
masses are more concentrated than the surrounding tissues, we designed a weighted average filter and a threedimensional
Iris filter to operate on the three-dimensional images. The basic process is: After weighted average filtering
and iris filtering, a thresholding is applied to extract suspicious regions. Next, after morphological processing, suspicious
regions are sorted based on their average Iris filter responses and the top 10 candidates are selected as detection results.
The detection results are marked out and provided to radiologists as CAD system output. In our experiment, our method
detects 12 mass locations out of 14 pathology-proven malignant clinical cases.