[1]吴一全,纪守新.灰度熵和混沌粒子群的图像多阈值选取[J].智能系统学报,2010,5(6):522-529.
　WU Yi-quan,JI Shou-xin.Multithreshold selection for an image based on gray entropy and chaotic particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2010,5(6):522-529.
点击复制

灰度熵和混沌粒子群的图像多阈值选取

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 5 期数: 2010年第6期 页码: 522-529 栏目: 学术论文—人工智能基础 出版日期: 2010-12-25
Title:
Multithreshold selection for an image based on gray entropy and chaotic particle swarm optimization
文章编号:
1673-4785(2010)06-0522-08
作者:
吴一全1,2，纪守新1
南京航空航天大学 信息科学与技术学院，江苏 南京210016；
2 南京大学 计算机软件新技术国家重点实验室，江苏 南京 210093
Author(s):
WU Yi-quan1,2, JI Shou-xin1
1.School of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; 
2.State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
关键词:
图像分割; 阈值选取; 灰度熵; 量化图像直方图; 多阈值; 混沌小生境粒子群优化
Keywords:
image segmentation;  threshold selection;  gray entropy;  quantified image histogram;  multithreshold;  particle swarm optimization of chaotic niche
分类号:
TP391.41； TN911.73
文献标志码:
A
摘要:
最大Shannon熵阈值选取方法仅仅依赖于图像灰度直方图的概率信息，而没有直接考虑类内灰度级的均匀性.为此提出了最大灰度熵的阈值选取方法.首先给出了灰度熵的定义及其单阈值选取方法，该灰度熵与现有的仅基于直方图分布的最大Shannon熵不同，直接反映了类内灰度级的均匀性；其次导出了量化图像直方图的灰度熵单阈值选取公式；最后将灰度熵单阈值选取推广到多阈值选取，提出了相应的快速递推算法，并进一步采用混沌小生境粒子群优化算法寻找最佳多阈值.实验结果表明，与最大Shannon熵单阈值选取和基于粒子群的最大Shannon熵多阈值选取方法相比，所提出方法的分割图像边缘、纹理更为准确，视觉效果明显改善.
Abstract:
The method of threshold selection based on maximal Shannon entropy depends only on the probability information from a gray image histogram and does not immediately consider the uniformity of the gray scale within the cluster. Considering these facts, a method of threshold selection based on maximal gray entropy was proposed. First, gray entropy was defined and the method of single threshold selection was given. Being different from maximal Shannon entropy based only on histogram distribution, the gray entropy reflects the uniformity of the gray scale immediately within the cluster. Then, the formulae of gray entropy based single threshold selection of a quantized image histogram were derived. Finally, the method of single threshold selection based on gray entropy was extended to multithreshold selection. A corresponding fast recurring algorithm was proposed. Furthermore, a particle swarm optimization algorithm with a chaotic niche was adopted to find the best multithreshold. Many experimental results show that, compared with the methods of single threshold selection based on maximal Shannon entropy and multithreshold selection based on maximal Shannon entropy with particle swarm optimization, segmented images of the suggested method are more accurate in edge and texture, and their visual effect is improved significantly.
参考文献/References:
［1］陈小波, 程显毅.一种基于MAS 的自适应图像分割方法［J］. 智能系统学报, 2007, 2(4): 8085.
CHEN Xiaobo, CHENG Xianyi. An adaptive image segmentation technique based on multiAgent system［J］. 　CAAI Transactions on Intelligent Systems, 2007, 2(4): 8085
［2］王科俊, 郭庆昌. 基于粒子群优化算法和改进的Snake 模型的图像分割算法［J］. 智能系统学报, 2007, 2(1): 5358.
WANG Kejun, GUO Qingchang. Image segmentation algorithm based on the PSO and improved Snake model［J］. CAAI Transactions on Intelligent Systems, 2007, 2(1): 5358.
［3］KAPUR J N, SAHOO P K, WONG A K C. A new method for greylevel picture thresholding using the entropy of the histogram［J］. Computer Vision, Graphics and Image Processing, 1985, 29(1): 273285.
［4］曹力, 史忠科. 基于最大Shannon熵原理的多阈值自动选取新方法［J］. 中国图象图形学报, 2002, 7(5): 461465.
CAO Li, SHI Zhongke. An automatic multilevel thresholding method based on maximal entropy［J］. Journal of Image and Graphics, 2002, 7(5): 461465.
［5］CHANG H C, CHEN J R, LI J G. Threshold selection based on fuzzy cpartition entropy approach［J］. Pattern Recognition, 1998, 31 (7): 857870.
［6］罗希平, 田捷. 用最大Shannon熵原则作多阈值选择的条件迭代算法［J］. 软件学报, 2000, 11(3): 379385.
LUO Xiping, TIAN Jie. The ICM algorithm for multilevel threshold selection by maximal entropy criterion［J］. Journal of Software, 2000, 11(3): 379385.
［7］王毅,牛奕龙,田沄,等.基于改进遗传算法的最佳熵多阈值三维医学图像分割算法［J］.西北工业大学学报, 2007, 25(3): 442445.
WANG Yi, NIU Yilong, TIAN Yun, et al. A more stable and accurate genetic algorithm for segmentation of 3D medical images［J］. Journal of Northwestern Polytechnical University, 2007, 25(3): 442445.
［8］WANG Y, FAN Y Y, NIU Y L, et al. Effective immune genetic algorithm for segmentation of 3D brain images［J］. Journal of System Simulation, 2008, 20(15): 41364140.
［9］韦苗苗, 江铭炎. 基于粒子群优化算法的多阈值图像分割［J］. 山东大学学报：工学版, 2005, 35(6)： 118121.
WEI Miaomiao, JIANG Mingyan. Multilevel thresholding methods for image segmentation based on particle swarm optimization［J］. Journal of Shandong University: Engineering Science, 2005, 35(6): 118121.
［10］严学强, 叶秀清, 刘济林,等. 基于量化图像直方图的最大Shannon熵阈值处理算法［J］. 模式识别与人工智能, 1998, 11(3): 352358.
YAN Xueqiang, YE Xiuqing, LIU Jilin, et al. Maximal entropy image thresholding algrithm based on the histogram defined on quantization image［J］. Pattern Recognition and Artificial Intelligence, 1998， 11(3): 352358.
［11］贾东立,张家树.基于混沌变异的小生境粒子群优化算法［J］. 控制与决策, 2007, 22(1):117120.
JIA Dongli, ZHANG Jiashu. Niche particle swarm optimization combined with chaotic mutation［J］. Control and Decision, 2007, 22(1): 117120.
相似文献/References:
[1]王科俊,郭庆昌.基于粒子群优化算法和改进的Snake模型的图像分割算法[J].智能系统学报,2007,2(1):53.
　WANG Ke-jun,GUO Qing-chang.Image segmentation algorithm based on the PSO and improved Snake model[J].CAAI Transactions on Intelligent Systems,2007,2():53.
[2]陈小波,程显毅.一种基于MAS的自适应图像分割方法[J].智能系统学报,2007,2(4):80.
　CHEN Xiao-bo,CHENG Xian-yi.An adaptive image segmentation technique based on multiAgent system[J].CAAI Transactions on Intelligent Systems,2007,2():80.
[3]刘咏梅,代丽洁.基于空间位置约束的K均值图像分割[J].智能系统学报,2010,5(1):67.
　LIU Yong-mei,DAI Li-jie.An improved method of Kmeans image segmentation based on spatial position information[J].CAAI Transactions on Intelligent Systems,2010,5():67.
[4]尚倩,阮秋琦,李小利.双目立体视觉的目标识别与定位[J].智能系统学报,2011,6(4):303.
　SHANG Qian,RUAN Qiuqi,LI Xiaoli.Target recognition and location based on binocular stereo vision[J].CAAI Transactions on Intelligent Systems,2011,6():303.
[5]胡光龙,秦世引.动态成像条件下基于SURF和Mean shift的运动目标高精度检测[J].智能系统学报,2012,7(1):61.
　HU Guanglong,QIN Shiyin.High precision detection of a mobile object under dynamic imaging based on SURF and Mean shift[J].CAAI Transactions on Intelligent Systems,2012,7():61.
[6]马慧,王科俊.采用旋转校正的指静脉图像感兴趣区域提取方法[J].智能系统学报,2012,7(3):230.
　MA Hui,WANG Kejun.A region of interest extraction method using rotation rectified finger vein images[J].CAAI Transactions on Intelligent Systems,2012,7():230.
[7]尹雨山,王李进,尹义龙,等.回溯搜索优化算法辅助的多阈值图像分割[J].智能系统学报,2015,10(1):68.[doi:10.3969/j.issn.1673-4785.201410008]
　YIN Yushan,WANG Lijin,YIN Yilong,et al.Backtracking search optimization algorithm assisted multilevel threshold for image segmentation[J].CAAI Transactions on Intelligent Systems,2015,10():68.[doi:10.3969/j.issn.1673-4785.201410008]
[8]龙鹏,鲁华祥.方差不对称先验信息引导的全局阈值分割方法[J].智能系统学报,2015,10(5):663.[doi:10.11992/tis.201412022]
　LONG Peng,LU Huaxiang.Global threshold segmentation technique guided by prior knowledge with asymmetric variance[J].CAAI Transactions on Intelligent Systems,2015,10():663.[doi:10.11992/tis.201412022]
[9]刘恋,常冬霞,邓勇.动态小生境人工鱼群算法的图像分割[J].智能系统学报,2015,10(5):669.[doi:10.11992/tis.201501001]
　LIU lian,CHANG Dongxia,DENG Yong.An image segmentation method based on dynamic niche artificial fish-swarm algorithm[J].CAAI Transactions on Intelligent Systems,2015,10():669.[doi:10.11992/tis.201501001]
[10]廖翠萃,李敏,梁久祯,等.数值求解优化问题在活动轮廓模型上的应用[J].智能系统学报,2015,10(6):886.[doi:10.11992/tis.201507037]
　LIAO Cuicui,LI Min,LIANG Jiuzhen,et al.Application of a numerical solution to the optimization problem in the active contour model[J].CAAI Transactions on Intelligent Systems,2015,10():886.[doi:10.11992/tis.201507037]
[11]吴一全,王凯,曹鹏祥.蜂群优化的二维非对称Tsallis交叉熵图像阈值选取[J].智能系统学报,2015,10(1):103.[doi:10.3969/j.issn.1673-4785.201403040]
　WU Yiquan,WANG Kai,CAO Pengxiang.Two-dimensional asymmetric tsallis cross entropy image threshold selection using bee colony optimization[J].CAAI Transactions on Intelligent Systems,2015,10():103.[doi:10.3969/j.issn.1673-4785.201403040]
[12]马英辉,吴一全.利用混沌布谷鸟优化的二维Renyi灰度熵图像阈值选取[J].智能系统学报,2018,13(1):152.[doi:10.11992/tis.201607004]
　MA Yinghui,WU Yiquan.Two-dimensional Renyi-gray-entropy image threshold selection based on chaotic cuckoo search optimization[J].CAAI Transactions on Intelligent Systems,2018,13():152.[doi:10.11992/tis.201607004]

备注/Memo

收稿日期：2010-04-12.
基金项目：国家自然科学基金资助项目（60872065）;航空科学基金资助项目（20105152026）；南京大学计算机软件新技术国家重点实验室开放基金资助项目（KFKT2010B17）.
通信作者：吴一全. E-mail: nuaaimage@yahoo.com.cn.
作者简介：
吴一全, 男, 1963年生, 教授, 博士, 主要研究方向为图像处理与模式识别、目标检测与跟踪、智能信息处理等. 在国内外核心刊物和国际学术会议上发表学术论文90余篇.
纪守新，男，1984年生，硕士研究生，主要研究方向为图像处理与目标检测等.

更新日期/Last Update: 2011-03-03
Copyright © 《 智能系统学报》 编辑部
地址：（150001）黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话：0451- 82534001、82518134　邮箱：tis@vip.sina.com