摘要
针对传统活动轮廓模型无法精确分割强度不均匀图像,并且对尺度参数比较敏感的问题,提出了一种基于区域信息的自适应尺度的活动轮廓模型。根据图像的局部熵构建自适应尺度算子,利用图像的局部强度聚类性质构建能量函数。使用一组平滑基函数的线性组合来表示偏移场,这样可以增加模型的稳定性。通过最小化该能量,所提模型能够同时分割图像和估计偏移场,并且估计的偏移场可以用于强度不均匀校正。实验结果表明,与其它4种模型相比,该模型拥有更高的分割精确度,且分割结果对水平集函数的初始化和噪声具有鲁棒性。
Aiming at the problem that the traditional active contour model cannot accurately segment images with intensity inhomogeneity and is sensitive to scale parameters,an adaptive scale active contour model based on region information is proposed.The adaptive scale operator is constructed based on the local entropy of the image,and the energy function is constructed by using the local intensity clustering property of the image.A linear combination of a set of smooth basis functions is used to represent the bias field to increases the stability of the model.By minimizing this energy,the proposed model is able to simultaneously segment the image and estimate the bias field,and the estimated bias field can be used for intensity inhomogeneity correction.Experimental results show that,compared with the other four models,the proposal has higher segmentation accuracy,and the segmentation result is robust to the initial level set function and noise in the image.
作者
赵仁和
王军锋
ZHAO Ren-he;WANG Jun-feng(School of Science,Xi’an University of Technology,Xi’an 710054,China)
出处
《计算机工程与科学》
CSCD
北大核心
2020年第6期1043-1048,共6页
Computer Engineering & Science
基金
西安市科技创新引导项目(201805037YD15CG21(7))。
关键词
图像分割
活动轮廓模型
水平集方法
自适应尺度
局部强度聚类
image segmentation
active contour model
level set method
adaptive scale
local intensity clustering
