Fang Jiangxiong, Liu Hesheng, Liu Huaxiang, Zhang Liting, Liu Jun
Fundamental Science on Radioactive Geology and Exploration Technology Laboratory, East China University of Technology, Nanchang 330013, China; Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG, Nanchang 330013, China; School of Geophysics and Measure Control Technology, East China University of Technology, Nanchang 330013, China.
Fundamental Science on Radioactive Geology and Exploration Technology Laboratory, East China University of Technology, Nanchang 330013, China.
Comput Math Methods Med. 2016;2016:1064692. doi: 10.1155/2016/1064692. Epub 2016 Dec 13.
This paper presents a novel fuzzy region-based active contour model for image segmentation. By incorporating local patch-energy functional along each pixel of the evolving curve into the fuzziness of the energy, we construct a patch-based energy function without the regurgitation term. Its purpose is not only to make the active contour evolve very stably without the periodical initialization during the evolution but also to reduce the effect of noise. In particular, in order to reject local minimal of the energy functional, we utilize a direct method to calculate the energy alterations instead of solving the Euler-Lagrange equation of the underlying problem. Compared with other fuzzy active contour models, experimental results on synthetic and real images show the advantages of the proposed method in terms of computational efficiency and accuracy.
本文提出了一种用于图像分割的基于模糊区域的新型主动轮廓模型。通过将沿演化曲线的每个像素的局部块能量泛函纳入能量的模糊性中，我们构建了一个没有回退项的基于块的能量函数。其目的不仅是使主动轮廓在演化过程中无需周期性初始化就能非常稳定地演化，而且还能减少噪声的影响。特别是，为了避免能量泛函的局部最小值，我们采用直接方法来计算能量变化，而不是求解潜在问题的欧拉 - 拉格朗日方程。与其他模糊主动轮廓模型相比，在合成图像和真实图像上的实验结果表明了该方法在计算效率和准确性方面的优势。
