Breast-region segmentation in MRI using chest region atlas and SVM

An important step for computerized analysis of breast magnetic resonance imaging (MRI) is segmentation
of the breast region. Due to the similar signal intensity of broglandular tissue and the chest wall, the segmentation
process is difficult for breasts with broglandular tissue connected to the chest wall. In order to overcome this challenge,
a new framework is presented that relies on a chest region atlas. The proposed method rst detects the approximated
breast{chest wall boundary using an intensity-based operation. A support vector machine (SVM) then determines the
connectivity of broglandular tissue to the chest wall by the extracted features from the obtained breast{chest wall
boundary. Finally, the obtained breast{chest wall boundary is accurately re ned using the geometric shape of the chest
region, which is obtained by an atlas-based segmentation method. The proposed method is validated using a dataset of
5964 breast MRI images from 126 women. The Dice similarity coefficient (DSC), total overlap (TO), false negative (FN),
and false positive (FP) values are calculated to measure the similarity between automatic and manual segmentation
results. Our method achieves DSC, TO, FN, and FP values of 96.46%, 96.41%, 3.59%, and 3.51%, respectively. The
results prove the effectiveness of the presented algorithm for breasts with different sizes, shapes, and density patterns.