基于模糊c均值聚类和图切优化的多相水平集图像分割方法

Lin Song, M. Gao, Sa Wang, Shuxia Wang
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引用次数: 1

摘要

多相水平集模型对初始轮廓曲线敏感,在多目标分割过程中计算量大。提出了一种新的多相场景图像分割方法,利用模糊c均值聚类算法对图像进行粗分割,初始化多相水平集函数,并应用图切算法获取多相输出图像。该方法有效地降低了多相水平集算法对初始轮廓的敏感性,更容易通过图切算法获得多相输出图像。同时,由于采用了图割算法,多相水平集函数快速收敛到最小能量值,计算量小,计算效率高。实验表明,该方法具有较好的分割效果和较高的分割效率。
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An Image Segmentation Method by Combining Fuzzy C-Means Clustering and Graph Cuts Optimization for Multiphase Level Set Algorithms
Multiphase level set model is sensitive to initial contour curve and has huge computation in the process of the multiple objects' segmentation. This paper presents a novel Image segmentation method for multiphase scenario, which initialize the multiphase level set function by coarse image segmentation using fuzzy C-means clustering algorithm and apply graph cut algorithm to acquire multiphase output image. The method effectively reduces the sensitivity of the multiphase level set algorithm to initial contour and is easier to gain the multiphase output image by graph cut algorithm. At the same time, because of using the graph cut algorithm, the multiphase level set function quickly converge to the minimum energy value with small amount of calculation and high computational efficiency. The experiments show that this method has better segmentation effect and higher efficiency of image segmentation.
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