Sigmoid gradient vector flow for medical image segmentation
Yuhua Yao, Lixiong Liu, Lejian Liao, Ming Wei, Jianping Guo, Yinghui Li · 2012
Active contour model has a good performance in consecutive boundary extraction for medical images. The gradient vector flow (GVF) field is one of the most popular external forces that can increase the capture range and converge to concavities, although it is sensitive to image noise and easy to leak in weak edge. Here we propose a novel sigmoid gradient vector flow (SGVF) force model for improving contour performance. This novel external force field is insensitive to noises and may prevent the weak edge leakage. To further illustrate the advantages associated with the proposed GVF field formulation, synthetic images and real images are conducted when the proposed method is applied in ultrasound image and magnetic resonance image for suppressing noise and extracting the weak edges. Experimental results demonstrate that the proposed method leads to more accurate segmentation.