A new active contour model-based segmentation approach for accurate extraction of the lesion from breast DCE-MRI
Yiping Liu, Hui Liu, Zuowei Zhao, Lina Zhang, Xiang Liu · 2013
Breast lesion segmentation is an essential step in developing Computer-aided Diagnostic (CAD) system for the early detection of breast cancer. However, accurate lesion segmentation is challenging, since the lesions may have complicated topological structures and different intensity distributions. This paper describes a novel approach for breast lesion segmentation from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a level set-based active contour model. Its evolution is controlled by specially designed signed pressure function that only accounts for distribution of image background intensity, so solving the weak-edge-passed problem and improving the robustness to segment different lesions. To confirm the proposed approach, it is compared with different methods based on level set. Experiment results show that the proposed method leads to better results. On a total of 38 breast DCE-MRI studies, our method yields a mean difference (MAD) of 7.2561±12.4537 pixels and Jaccard index of 0.8564±0.0493 compared to manual ground truth segmentation.