基于模糊聚类双水平集的医学图像分割算法

Double Level Set Segmentation Algorithm of Medical Images Based on Fuzzy Clustering

医学图像分割对于医生诊断患者病情具有重要意义。然而,由于医学图像本身的特点,导致对于医学图像的分割具有相当大的难度,比如噪声多,边界模糊等。本文首先采用模糊C均值聚类方法对医学图像进行初始分割,获得大致的医学图像分割图,然后利用改进的水平集算法,分割图进行二次分割,这样可以大大减少水平集迭代次数,加快算法收敛时间,对于医生诊断病情具有重要意义。

Medical image segmentation is of great significance for doctor diagnosis patients. However,due to the characteristics of medical image,it has caused considerable difficulty for medical image segmentation,such as noise,fuzzy boundaries and so on. Firstly,fuzzy c-Mean clustering method for medical image segmentation of medical image to obtain initial,rough segmentation map,and then by using the improved level set algorithm,twice segregate segmentation map,which can greatly reduce the level set number of iterations,accelerate the convergence time,it has an important significance for doctors to diagnose the disease.