Mahalanobis distance-based FCM image segmentation algorithm

doi:10.3778/j.issn.1002-8331.2010.01.045

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (1): 147-149.DOI: 10.3778/j.issn.1002-8331.2010.01.045

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

Mahalanobis distance-based FCM image segmentation algorithm

WANG Jian-ying¹，SUN De-shan¹，ZHANG Yong²

1.School of Mathematics，Liaoning Normal University，Dalian，Liaoning 116029，China
2.School of Computer and Information Technology，Liaoning Normal University，Dalian，Liaoning 116081，China

Received:2009-10-19 Revised:2009-11-26 Online:2010-01-01 Published:2010-01-01
Contact: WANG Jian-ying

基于马氏距离的FCM图像分割算法

王建英¹，孙德山¹，张永²

1.辽宁师范大学数学学院，辽宁大连 116029
2.辽宁师范大学计算机与信息技术学院，辽宁大连 116081

通讯作者: 王建英

Abstract

Abstract: Fuzzy C-Means（FCM） clustering is one of well-known unsupervised clustering techniques，which has been widely used in automated image segmentation. However，most of fuzzy partition clustering algorithms are based on Euclidean distance function which can only be used to detect spherical structural clusters，and have disadvantages in runtime. This paper presents a Mahalanobis distance-based fuzzy C-means clustering image segmentation algorithm. Experiments show that the proposed method improves the segmentation runtime on the basis of segmentation qualities. Experimental results show that the proposed method is effective.

Key words: fuzzy C-means clustering, image segmentation, Mahalanobis distance

摘要： 基于模糊C均值聚类的图像分割是应用较为广泛的方法之一，但大多数模糊C均值聚类方法都是基于欧式距离，且存在运算时间过长等问题。提出了一种基于Mahalanobis距离的模糊C均值聚类图像分割算法。实验分析表明，提出的算法在保证分割质量的前提下，能较快提高分割速度。实验结果表明了该方法的有效性。

关键词: 模糊C均值聚类, 图像分割, 马氏距离

CLC Number:

TP18

WANG Jian-ying¹，SUN De-shan¹，ZHANG Yong². Mahalanobis distance-based FCM image segmentation algorithm[J]. Computer Engineering and Applications, 2010, 46(1): 147-149.

王建英¹，孙德山¹，张永². 基于马氏距离的FCM图像分割算法[J]. 计算机工程与应用, 2010, 46(1): 147-149.

[1]	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.
[2]	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.
[3]	PAN Peixin, PAN Zhongliang. Active Contour Image Segmentation Combined with Saliency [J]. Computer Engineering and Applications, 2021, 57(8): 225-230.
[4]	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.
[5]	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.
[6]	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.
[7]	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.
[8]	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.
[9]	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.
[10]	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.
[11]	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.
[12]	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.
[13]	YANG Yong, GUO Ling, YE Yangdong. Approach of Total Variation Flow Boundary Integrated with M2GGD for Natural Image Segmentation [J]. Computer Engineering and Applications, 2021, 57(11): 202-210.
[14]	LU Yuanyuan, FENG Hao, LI Jing. Active Contour Image Segmentation Combined with Statistical Modeling of Distribution Metrics [J]. Computer Engineering and Applications, 2020, 56(7): 228-233.
[15]	ZHANG Shujie, ZHENG Liping, HAN Qing, ZHANG Han. Pictures to Clothes：Method of Quickly Generating Costumes for Virtual Characters [J]. Computer Engineering and Applications, 2020, 56(7): 234-239.

Mahalanobis distance-based FCM image segmentation algorithm

基于马氏距离的FCM图像分割算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics