结合局部特征和全局信息的自适应活动轮廓模型

Adaptive active contour model integrating global and local image fitting energy

提出一种新的基于全局图像信息和局部图像特征的活动轮廓分割模型。模型的总能量函数主要包括3项:全局能量项、局部能量项和自适应调节项。其中,全局能量项整合了图像的全局信息,局部能量项则考虑了图像的局部特征,而二者的权重会根据上下文内容自适应调整。由于在模型中充分利用了图像全局信息和局部特征,因而有效地提高了分割的精度。此外,加入了凸优化技术,以获取模型的全局最优解。最后,采用Split-Bregman方法进行快速求解,使得模型的分割效率大大提高。实验结果表明,该模型对初始化具有较好的鲁棒性,在分割精度上有了较大的提升,特别是分割速度比C-V模型快1.5倍到2倍。

A new active contour model based on global and local image information is proposed for image segmentation. The energy functional for the proposed model consists of three terms, i.e. , the global intensity fitting term, the local in-tensity fitting term, and the adaptive parameter term. The global intensity fitting term incorporates global image information and the local intensity fitting term uses local contextual information. The weighting factor between the global and local inten-sity fitting term is adaptive by the image content. By incorporating the local and global image information into the proposed model, the images can be efficiently segmented. In addition, convex optimization is added to the new model to get the glob-al minima. Finally, the Split-Bregman method can effectively improve the segmentation speed. Experimental results demon-strate that the proposed algorithm is robust to the choice of initialization values, can get the more accurate segmentation re-suit, and especially is about 1.5 to 2 times faster than the C-V （ Chan ＆ Vese） model.