摘要
本文从理论上分析了无需重新初始化的水平集方法的主动轮廓图像分割模型,此模型有很大的优越性,但对于目标与背景对比度较小这种情况不能得到一个好的分割效果。该模型应用于CT图像中肝脏的分割时,主动轮廓曲线会跨越肝脏边界从而导致错误的分割结果。通过修正边缘检测函数,加强了其在目标边界处的约束效果,使得主动轮廓曲线在目标物体边界处停止演化,这样能够准确的将肝脏分割出来,保证了分割的正确性。实验证明了该方法的可行性。
Firstly,an image segmentation method based on level set evolution without re-initialization is studied.It has relative great superiority.But we can t get a good result when the contrast of object and background is small.The active contour curve goes beyond the liver edges in the CT image.Secondly,the method is improved by revising the edge indicator function near edges.In this case,the restriction on the edges would be strengthened,and the active contour curve will stop on the edges.Then,the edges of the object can be captured more accurately. The numerical experiments show the advantage of the improved model.
出处
《微计算机信息》
2010年第1期104-105,108,共3页
Control & Automation
基金
基金申请人:吴效明
项目名称:基于无线穿戴式检测技术的社区数字医疗健康服务系统
基金颁发部门:广东省科学技术厅(2007B031302003)
关键词
水平集
图像分割
边缘检测函数
level set method
image segmentation
edge indicator function
