中文说明：此方法也会给与其他算法更好的结果。方法可以相比传统以及新的方法 （但他们还有更少的噪声鲁棒性） 等聚类方法 （基于 k-均值，模糊 c-均值等） 水平集的方法 （如快速和鲁棒性的级别设置基于模糊聚类和 LBM 等）、 基于图模型的方法 （图割等）、 区域分割和合并为基础的方法 (分水岭算法等。）。
English Description：
This method will also give better results in comparison with other algorithms. The methods can be compared with traditional as well as new methods (but they are also less noise robust) such as clustering methods (based on k-means, fuzzy c means etc.), level set methods (e.g.- fast and robust level set based on fuzzy clustering and LBM etc.), graph based methods (graph cut etc.) and region split & merging based methods (watershed based etc.).