一种新的用于图像分割的活动轮廓模型

史娜; 潘晋孝

您当前的位置：

首页 >

文章列表页 >

一种新的用于图像分割的活动轮廓模型

电子与电子信息 | 更新时间：2026-09-14

- 一种新的用于图像分割的活动轮廓模型
- A New Active Contour Model for Image Segmentation
- 中北大学学报（自然科学版） 2012年33卷第2期页码：201-206
- 作者机构：
  
  中北大学理学院
- 作者简介：
- 基金信息：
  
  国防重点实验室基金资助项目(9140c1204051010);山西省自然科学基金资助项目(2010011002-1)
- DOI：
  中图分类号：
- 纸质出版：2012
- 稿件说明：
移动端阅览
史娜, 潘晋孝. 一种新的用于图像分割的活动轮廓模型[J]. 中北大学学报（自然科学版）, 2012,33(2):201-206.

史娜, 潘晋孝. A New Active Contour Model for Image Segmentation[J]. 2012, 33(2): 201-206.
史娜, 潘晋孝. 一种新的用于图像分割的活动轮廓模型[J]. 中北大学学报（自然科学版）, 2012,33(2):201-206. DOI：

史娜, 潘晋孝. A New Active Contour Model for Image Segmentation[J]. 2012, 33(2): 201-206. DOI：

摘要

为了从灰度异质图像中更好地提取目标

本文提出了一种新的图像分割方法

采用测地时间函数作为局部二值拟合模型的核函数

并结合图像区域的局部灰度信息和全局灰度信息建立能量泛函.同时

符号函数惩罚项的引入避免了符号函数的重新初始化

而曲线长度调整项则保证了曲线演化的连续性和光滑性.通过变分水平集方法最小化新的能量泛函

得到曲线演化的梯度下降流.通过对医学CT图像进行分割实验

证明了该方法的可行性和优越性.

Abstract

In order to extract the object boundaries from the images of intensity inhomogeneity

a new image segmentation method is proposed.The new energy functional is set up by local and global intensity information

and the geodesic time is defined as kernel function of local binary fitting model.For there introducing the penalty term of signed distance function

the reinitialization is no longer required.The arc length adjustment term has ensured the evolution with continuous and smooth curve.The gradient descent flow of curve evolution is obtained by energy minimization through the variation level set method.The feasibility and robustness of the proposed model had been certified by the experiments of medical CT images segmentation.

关键词

Keywords

references

Kass M,Witkin A,Terzopoulos D.Snakes:active contour models[J].International Journal of Computer Vision,1988,1(4):321-331.

Osher S,Sethian J A.Fronts propagating with cur-vature dependent speed:algorithms based on Hamilton-Jacobi formulations[J].Journal of Computational Physics,1988,79:12-49.

Li C M,Xu C Y,Gui C F,et al.Level set evolution without reinitialization:a new variational formulation[J].IEEE Conference on Computer Vision and Pattern Recognition,San Diego,2005:430-436.

Zheng Ying,Li Guangyao,Sun Xiehua,et al.Geo-metric active contours without reinitialization for image segmentation[J].Pattern Recognition,2009,42:1970-1976.

Wang Li,Li Chunming.Active contours by local and global intensity fitting energy with application to brain MR image segmentation[J].Computerized Medical Imaging and Graphics,2009,33:520-531.

Chan T,Vese L.Active contour without edges[J].IEEE Transaction on Image Processing,2001,10(2):266-277.

Li C M,Kao C,Gore J,et al.Implicit active contours driven by local binary fitting energy[J].IEEE Conference on Computer Vision and Pattern Recognition,2007:1-7.

Li C M,Kao C,Gore J,et al.Minimization of region-scalable fitting energy for image segmentation[J].IEEE Transactions on Image Processing,2008,17(10):1940-1949.

Wang Li,Li Chunming,Sun Quansen,et al.Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation[J].Computerized Medical Imaging and Graphics,2009,33:520-531.

Wang Xiaofeng,Huang Deshuang,Xu Huan.An efficient local Chan-Vase model for image segmentation[J].Pattern Recognition,2010,43:603-618.

Zhang Kaihua,Zhang Lei,Song Huihui,et al.Active contours with selective local or global segmentation:A new formulation and level set method[J].Image and Vision Computing,2010,28:668-676.

Zhang Kaihua,Song Huihui,Zhang Lei.Active contours driven by local image fitting energy[J].Pattern Recogniton,2010,43:1199-1206.

Wang Li,Chen Yunjie,Pan Xiaohua,et al.Level set segmentation of brain magnetic resonance images based on local Gaussian distribution fitting energy[J].Journal of Neuroscience Methods,2010,188:316-325.

刘秀平,常先堂,李治隆.一种基于边缘和区域信息的变分水平集图像分割方法[J].大连理工大学学报,2008,48(5):754-758.Liu Xiuping,Chang Xiantang,Li Zhilong.A variational level set method of image segmentation based on boundary and region information[J].Journal of Dalian University of Technology,2008,48(5):754-758.(in Chinese)

孔丁科,汪国昭.用于图像分割的边界保持局部拟合模型[J].浙江大学学报(工学版),2010,44(12):2236-2240.Kong Dingke,Wang Guozhao.Edge-preserving local fitting model for image segmentation[J].Journal of Zhejiang University(Engineering Science),2010,44(12):2236-2240.(in Chinese)

Soille P.Generalized geodesy via geodesic time[J].Pattern Recognition Letters,1994,15(12):1235-1240.

浏览量

163

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于改进Kmeans算法的玉米叶片图像分割

基于改进的水平集模型工业零件图像分割方法

基于改进的模糊C均值聚类算法的颗粒种子图像分割方法

一种改进的FCM聚类算法在噪声图像分割中的应用

基于模糊集和Otsu理论的PCB图像分割算法