基于灰度特征统计的可变区域图像分割算法  

Variable Domain Algorithm for Image Segmentation Using Statistical Models Based on Intensity Features

作　　者：高晓亮[1] 王志良[1] 刘冀伟[1] 崔朝辉[1] 王鲁[1] 

机构地区：[1]北京科技大学信息工程学院,北京100083

出　　处：《光学学报》2011年第1期190-195,共6页Acta Optica Sinica

基　　金：国家863计划(2007AA01Z160)资助课题

摘　　要：图像分割技术是计算机视觉低层次领域中的一项重要内容,是对图像进行分析和模式识别的基本前提,目前已被广泛地应用于诸多领域如医学图像和遥感图像等。同时,它也是图像处理中的一个难点。提出了一种可变区域的分割算法,利用基于全局灰度统计信息的活动轮廓模型进行曲线演化,并使用水平集表示轮廓。通过不断改变和缩小分割区域的策略,利用邻域替代算法,将分割过程分为多个阶段进行。这种算法的优点在于,可以自动地完成工作而无需人工干预。实验结果表明,图像中具有复杂结构的目标物体能够被准确而且快速地分割出来;与现有的方法相比,分割速度有了较为明显的提高。Image segmentation technology is an important part of the lower level of computer vision.It′s also a basic precondition for image analysis and pattern recognition.It has been widely used in many fields such as medical images and remote sensing images.Meanwhile,image segmentation is a difficulty in image processing as well.Aiming at medical imagery,a novel variational domain approach to curve evolution for image segmentation is proposed based on a statistical active contour model using level sets.The essential idea is to re-define the computing domain in image repeatedly by separating the segmentation procedure into several individual phases.By our algorithm,the work can be done automatically without manual intervention.Moreover,compared with current methods,the rapidity can be enhanced effectively for the objects with complicated topology.

关 键 词：机器视觉 可变区域 活动轮廓 统计模型 邻域替代 图像分割 水平集 

分 类 号：O436[机械工程—光学工程]