基于聚类和改进型水平集的图像分割算法

Image Segmentation Algorithm Based on Clustering and Improved Double Level Set

针对医学图像中通常伴有噪声、多目标的问题,传统水平集无法将图像中的多目标完全分割出来,提出了基于抑制式模糊聚类算法的改进型双水平集模型。首先,利用聚类算法对医学图像进行预分割降噪,通过标准化互信息准则(NMI)判断聚类是否达到满意效果,进而改良聚类算法,再由增加惩罚项的改进型双水平集进行二次分割。实验结果表明,该方法能够降低图像的噪声和算法的敏感性,水平集无需重新初始化,大大减少了计算量和迭代次数,该模型能将伴有噪声的多目标医学图像完全分割出来,获得了预期的分割效果。

Usually,medical image accompanied by noise with a multi-objective problem,can not be separated completely by traditional level set in the image with multiple targets.This paper proposed a model based on inhibiting type of fuzzy clustering algorithm and modified double level set.First of all,the clustering algorithm is used for pre segmentation of medical image noise reduction,which can determine whether a cluster achieves satisfied effect through standardized rule of normalized mutual information(NMI),thus improving clustering algorithm.The improved double level set with punishment item is given a second segmentation finally.The experimental results show that the method can reduce the noise of the image and the sensitivity of the algorithm,without reinitialize level set,reducing the amount of calculation and the number of iteration greatly.The model can separate medical image including noise and multiple objects completely,obtaining the expected effect of segmentation.