一种新的灰度非均匀图像分割模型

A New Segmentation Model of Gray Non- uniform Images

基于局部区域的主动轮廓分割模型在针对灰度非均匀图像进行分割时,容易受到初始轮廓曲线位置的影响,且基于水平集模型的数值实现速度较慢。为此,提出一种新的图像分割模型。该模型采用局部符号差能量项作为曲线演化的驱动力,为减少模型对初始轮廓曲线位置的依赖,采用全局凸分割策略,得到一个离散化的凸分割模型,该模型包含Mumford-Shah分割模型中的二次光滑项,使分割后的区域更加平滑,使用split Bregman迭代算法进行数值实现。实验结果表明,与局部二值拟合模型、局部符号差能量模型相比,该模型能对灰度非均匀图像进行较准确的分割,具有较快的运算速度和较好的鲁棒性。

Local region-based Active Contour Model（ ACM） is easily influenced by the location of the initial curves when it segments images with intensity inhomogeneity and its numerical implementation based on Level Set（ LS） method is lower. For this,a new segmentation model is proposed in this paper. The model includes the Local Signed Difference （ LSD） energy as data driven term for curve evolution. In order to reduce the dependence on the location of the initial curve,a Globally Convex Segmentation （ GCS） scheme is used to derive a discrete convex segmentation model. The new model includes a second order smooth term from Mumford-Shah segmentation model to make the segmented regions smoother. It uses split Bregman iterations to get a fast numerical implementation. Compared with the Local Binary Fitting （ LBF） model,LSD model,experimental results show that the model can segment images with intensity inhomogeneity correctly,and is more efficient and more robust.