NUS Graduate School for Integrative Science and Engineering, Vision & Image Processing Lab, National University of Singapore, Singapore.
Comput Biol Med. 2011 Jan;41(1):1-10. doi: 10.1016/j.compbiomed.2010.10.007. Epub 2010 Nov 12.
The performance of the level set segmentation is subject to appropriate initialization and optimal configuration of controlling parameters, which require substantial manual intervention. A new fuzzy level set algorithm is proposed in this paper to facilitate medical image segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy clustering. The controlling parameters of level set evolution are also estimated from the results of fuzzy clustering. Moreover the fuzzy level set algorithm is enhanced with locally regularized evolution. Such improvements facilitate level set manipulation and lead to more robust segmentation. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.
水平集分割的性能取决于适当的初始化和控制参数的最优配置，这需要大量的人工干预。本文提出了一种新的模糊水平集算法，以方便医学图像分割。它能够通过空间模糊聚类直接从初始分割中演化而来。水平集演化的控制参数也是根据模糊聚类的结果来估计的。此外，模糊水平集算法还增强了局部正则化演化。这些改进使得水平集的操作更加方便，并导致更鲁棒的分割。对来自不同模态的医学图像进行了所提出算法的性能评估。结果证实了其在医学图像分割中的有效性。
