基于窄带Mumford—Shah模型的图像分割方法

在结合Mumford-Shah模型和水平集方法中的窄带解法优点的基础上，提出了一种新的图像分割模型。Mumford-Shah模型虽然具有良好的图像分割结果，但是其每次迭代过程都需要对所有图像数据进行计算，因而很费时，导致这种方法不适用于大的图像数据，特别是三维图像的分割。本文通过一种新的初始化方法把Mumford-Shah模型和水平集中的窄带解法结合在一起。这种新 的初始化方法是通过在特定条件下简化快速行进法得到的。通过去除快速步进法中费时的 排序过程，使得初始化的计算时间只有O（N）。窄带Mumford-Shah模型把分割计算限制在窄带范围内，避免了大量的计算，但取得了与原始的Mumford-Shah模型相同的分割效果。实验结果表明基于快速步进法的初始化方法是可行的，而窄一喧M－S分割模型一次迭代计算的时间比原M－S模型减少许多。

SEGMENTATION BASED ONMUMFORD-SHAH MODEL COMBINED WITH NARROW BAND

A segmentation model that combines the Mumford Shah(M S) model and narrow band scheme of level set was presented. The disadvantage of Mumford Shah model is computationally time consuming. In each step of its iteration, the data of whole image have to be renewed, which is unbearable for segmentation of large image or 3D image. Therefore, a fast segmentation model was introduce, which combines the M S model and narrow band scheme by a new initialization method. The new initialization method is based on fast marching method, and the computing time decreases to O(N) . In each step of iteration, the new segmentation model only deals with the data in a narrow band instead of the whole image. The experiments show that the two models can obtain almost the same segmentation result, but the computing time of new narrow band M S model is much less than that of M S model.