基于无需重新初始化水平集的肌肉组织序列图像分割  

Muscle Tissue Image Sequences Segmentation Based on Level Set Without Re-initialization

作　　者：王亚琼[1] WANG Ya-qiong（Sichuan Normal University,Chengdu,610101,China）

机构地区：[1]四川师范大学计算机科学学院,四川成都610101

出　　处：《电脑知识与技术》2010年第12期9812-9814,共3页Computer Knowledge and Technology

摘　　要：水平集方法已被广泛地应用在图像分割中,传统的水平集方法需要通过周期性的初始化水平集函数使得它一直保持在符号距离函数附近。在该文中,使用一种无需重新初始化水平集方法来分割肌肉组织序列图像,从而完全消除对水平集重新初始化的过程提高了图像序列分割的效率。Level set methods have been extensively used in image segmentation,while the traditional level set methods is numerically necessary to keep the evolving level set function close to a signed distance function by periodically re-initializing.In the paper,to use level set without re-initialization segmenting muscle tissue image sequences,therefore completely eliminates the need of the costly re-initialization procedure and enhances the efficiency of image sequences segmentation.

关 键 词：图像分割 水平集方法 活动轮廓模型 无需重新初始化 序列图像 

分 类 号：TP391[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]