基于聚类信息的活动轮廓图像分割模型  

Active Contour Model for Image Segmentation Based on Clustering Information

作　　者：李敏[1] 梁久祯[1] 廖翠萃 

机构地区：[1]江南大学物联网工程学院智能系统与网络计算研究所,无锡214122

出　　处：《模式识别与人工智能》2015年第7期665-672,共8页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.11401259;61170121);中央高校基本科研基金项目(No.JUSH-11407)资助

摘　　要：基于传统Chan-Vese(CV)模型,结合图像聚类信息,提出一种有效的活动轮廓模型图像分割方法.该方法首先改进CV模型的能量泛函,考虑图像的梯度信息,提高图像分割的精确度.其次在能量泛函中添加图像的聚类信息系数K,并使用图像的聚类信息实现对水平集轮廓曲线的自动初始化.在分割处理彩色图像时,为提高分割效率,对彩色RGB图像的三通道进行加权处理.最后为能量泛函添加正则项,避免水平集的重新初始化,完成对灰度图像及彩色图像的快速精确分割.实验表明该方法的有效性.Based on traditional Chan-Vese （CV） model, combining image clustering information, an effective active contour model for image segmentation is proposed in this paper. Firstly, the energy functional of CV model is improved, the gradient information of image is considered, and the accuracy of image segmentation is improved. Then, the coefficient K based on image clustering information is added in energy functional. And the image clustering information is used to initialize the level set curves automatically. In color image segmentation processing, weighting process on the RGB channel is proposed to improve the efficiency of segmentation. Finally, regularization term is added in energy functional to avoid re-initialization of the level set. The gray images and color images are segmented quickly and accurately. Experimental results shows the effectiveness of the proposed method.

关 键 词：CHAN-VESE模型 水平集方法 K-MEANS聚类 图像分割 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]