基于模糊聚类水平集的医学图像分割方法

医学图像分割是图像分割的一个重要应用领域,医学图像普遍存在高噪声、伪影、低对比度、灰度不均匀、不同软组织之间与病灶之间边界模糊等特点,因此运用聚类算法,结合李春明模型(LCM)和两相水平集分割方法(CV),首先选用合适的滤波器对医学图像进行去噪,然后使用模糊C均值算法(FCM)获得图像的先验模型；并对传统的CV模型进行改进,对图像进行细分割。实验表明,该模型可以解决图像高噪声、弱边界问题,并可以有效避免重新初始化,对边缘更加敏感,可提高分割精度,有效的抑制噪声,明显的减少迭代次数和时间,具有一定应用价值。。

Fuzzy Clustering Level Set Based Medical Image Segmentation Method

Medical image segmentation is an important application field of image segmentation,it widespreadly has high noise,artifacts,low contrast,uneven gray,fuzzy boundaries between different between soft tissue lesions and other characteristics,this paper used clustering algorithm,combined with LCM and two phase model level set method (CV),chose the appropriate filter for medical image denoising,then used the fuzzy c-means algorithm to get image prior model.And we improved the traditional CV model to fine the image segmentation.Experiments show that the model can solve the problem of high image noise and weak boundary,and can effectively avoid the re-initialization,and is more sensitive to the edge,improving the segmentation accuracy,suppressing noise effectively,significantly reducing the number of iterations and time,having certain application value.