Multistage processing procedure for 4D breast MRI segmentation.
Contrast-enhanced magnetic resonance imaging (MRI) is a novel approach to detect breast tumor, however, multiple 3D image sets need to be analyzed which causes unaffordable inspection tasks to physicians. Computer assistant detection is of great help to this situation and image segmentation is most important process of computer assistant detection. To segment the breast region from 3D MRI set, a multistage image processing procedure was proposed. Considering the morphological features of breast image, the image was divided into two parts to reduce the complexity of the segmentation procedure. For the anterior part of the image, the breast tissue was segmented from background using the region growing method. Meanwhile, the mean intensity of breast tissue was estimated. To segment the posterior part of the image, the estimated mean breast intensity was used to initialize an approximate boundary between breast and chest. The estimated intensity distribution of breast tissue was used to set the propagation term of following level set iterations. Based on this initialization, threshold-based 3D level set algorithm was used to seek the precise boundary between chest and breast. The level set algorithm achieves novel performance for the 3D segmentation of chest boundary. Based on the segmentation of one set of 3D image, the segmentation result was used as the initial boundary of following image sets to achieve automatic 4D segmentation. This multistage procedure greatly accelerates the convergence of the level set algorithm and reduces the chance of running into local minimum. Clinical data demonstrated that this processing procedure was effective for automatic segmentation of 4D breast MRI.
Qi
Wang
W
Department of Biomedical Engineering, Tsinghua University, Beijing, China.
Hui
Ding
D
Guang-zhi
Wang
W
D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation; D001940:Breast / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D013382:Subtraction Technique