Image multi-threshold method based on fuzzy mutual information

doi:10.3778/j.issn.1002-8331.2009.25.051

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (25): 166-168.DOI: 10.3778/j.issn.1002-8331.2009.25.051

• 图形、图像、模式识别 • Previous Articles Next Articles

Image multi-threshold method based on fuzzy mutual information

LIU Li-xin

Department of Electronics and Information Engineering，Xi’an Institute of Posts and Telecommunication，Xi’an 710121，China

Received:2009-04-28 Revised:2009-06-17 Online:2009-09-01 Published:2009-09-01
Contact: LIU Li-xin

多阈值模糊互信息图像分割方法

刘立新

西安邮电学院电子与信息工程系，西安 710121

通讯作者: 刘立新

Abstract

Abstract: The new image multi-threshold method based on fuzzy mutual information is proposed.First，fuzzy membership function is used in classical mutual information and defines the new concept of fuzzy mutual information.Second，the method of constructing fuzzy membership function is proposed in image segmentation based on fuzzy mutual information.Finally，the optimal thresholds of image segmentation based on fuzzy mutual information is obtained by means of chaos optimization method.Experimental results show that multi-threshold image segmentation method based on fuzzy mutual information is feasible.

Key words: image segmentation, threshold, mutual information, fuzzy membership function

摘要： 提出了多阈值模糊互信息图像分割新方法。该方法首先将模糊隶属度函数引入到传统互信息量中并定义模糊信息量概念；其次将模糊互信息量用于图像分割时给出具体隶属度函数的构造；最后采用混沌优化法来获得多阈值模糊互信息分割方法的最佳阈值。实验结果表明，提出的多阈值模糊互信息图像分割方法是有效的。

关键词: 图像分割, 阈值化, 互信息, 模糊隶属度

CLC Number:

TP391

LIU Li-xin. Image multi-threshold method based on fuzzy mutual information[J]. Computer Engineering and Applications, 2009, 45(25): 166-168.

刘立新. 多阈值模糊互信息图像分割方法[J]. 计算机工程与应用, 2009, 45(25): 166-168.

[1]	ZHAO Yang, ZHANG Junhua. Multi-scale Feature Fusion Method for Spinal X-Ray Image Segmentation [J]. Computer Engineering and Applications, 2021, 57(8): 214-219.
[2]	PAN Peixin, PAN Zhongliang. Active Contour Image Segmentation Combined with Saliency [J]. Computer Engineering and Applications, 2021, 57(8): 225-230.
[3]	DONG Peng, ZHOU Feng, ZHAO Congcong, WANG Yafei, MI Zetian, FU Xianping. Automatic Measurement of Underwater Sea Cucumber Size Based on Binocular Vision [J]. Computer Engineering and Applications, 2021, 57(8): 271-278.
[4]	SHI Chuntian, ZENG Yanyang, HOU Shouming. Summary of Application of Swarm Intelligence Algorithms in Image Segmentation [J]. Computer Engineering and Applications, 2021, 57(8): 36-47.
[5]	YU Duo, HUANG Yongdong. Hyperspectral Image Classification Based on SPCA and Domain Transform Recursive Filtering [J]. Computer Engineering and Applications, 2021, 57(4): 199-208.
[6]	PENG Jing, LUO Haoyu, ZHAO Gansen, LIN Chengchuang, YI Xusheng, CHEN Shaojie. Survey of Medical Image Segmentation Algorithm in Deep Learning [J]. Computer Engineering and Applications, 2021, 57(3): 44-57.
[7]	LIU Rui, DING Hui, SHANG Yuanyuan, SHAO Zhuhong, LIU Tie. COVID-19 Medical Imaging Dataset and Research Progress [J]. Computer Engineering and Applications, 2021, 57(22): 15-27.
[8]	QI Xiaoxiang, LI Min, ZHU Ying, SONG Yu, DU Weidong. Adaptive Region Segmentation of SAR Image Based on Edge Detection [J]. Computer Engineering and Applications, 2021, 57(22): 232-240.
[9]	LI Longzhu, LIN Yaojin, LYU Yan, LU Shun, WANG Chenxi. Online Streaming Feature Selection Algorithm Using Neighborhood Information Interaction [J]. Computer Engineering and Applications, 2021, 57(21): 102-108.
[10]	ZHOU Shaoguang, WU Hao, ZHAO Chanjuan, CHEN Renxi. Transfer Learning for Hyperspectral Image Classification Using Homogeneous Area Characteristics [J]. Computer Engineering and Applications, 2021, 57(21): 224-233.
[11]	LI Jiasheng, GUO Shuxu, ZHANG Lei, ZHENG Shuang, ZHANG Huimao, QIU Yunhai, LI Xueyan. Research of Segmentation and Detection of Liver Metastases from CT Images Based on Deep Network [J]. Computer Engineering and Applications, 2021, 57(2): 191-197.
[12]	DING Cheng, WENG Liguo, XIA Min, CUI Yichen, QIAN Junhao, LIU Jia. Multi-attention Mechanism Network Satellite Image Segmentation Algorithm [J]. Computer Engineering and Applications, 2021, 57(2): 223-229.
[13]	LI Zhenqiang, WANG Shucai, ZHAO Shida, BAI Yu. Cutting Methods of Sheep’s Trunk Based on Improved DeepLabv3+ and XGBoost [J]. Computer Engineering and Applications, 2021, 57(18): 263-269.
[14]	SUN Weijie, YANG Jun. Research on Remote Sensing Image Segmentation Based on Improved Simple Non-iterative Clustering [J]. Computer Engineering and Applications, 2021, 57(13): 185-192.
[15]	QIU Yunfei, GAO Huacong. Hybrid Filter and Improved Adaptive GA for Feature Selection [J]. Computer Engineering and Applications, 2021, 57(11): 95-102.

Image multi-threshold method based on fuzzy mutual information

多阈值模糊互信息图像分割方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics