An Optimized Level Set Method Based on QPSO and Fuzzy Clustering

被引：2

作者：

Yang, Ling ^{[1
,4
]}

Fu, Yuanqi ^{[1
]}

Wang, Zhongke ^{[2
]}

Zhen, Xiaoqiong ^{[1
]}

Yang, Zhipeng ^{[1
]}

Fan, Xingang ^{[1
,3
]}

机构：

[1] Chengdu Univ Informat Technol, Elect Engn Coll, Chengdu 610225, Sichuan, Peoples R China

[2] Chengdu Univ Informat Technol, Informat Secur Engn Coll, Chengdu 610225, Sichuan, Peoples R China

[3] Western Kentucky Univ, Dept Geog & Geol, Bowling Green, KY 42101 USA

[4] Chengdu Univ Informat Technol, CMA Key Lab Atmospher Sounding, Chengdu 610225, Sichuan, Peoples R China

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2019年 / E102D卷 / 05期

关键词：

image segmentation; fuzzy c-means clustering method (FCM); level set method (LSM); quantum particle swarm optimization (QPSO); QPSO-FLSM method; IMAGE; INFORMATION;

D O I：

10.1587/transinf.2018EDP7132

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A new fuzzy level set method (FLSM) based on the global search capability of quantum particle swarm optimization (QPSO) is proposed to improve the stability and precision of image segmentation, and reduce the sensitivity of initialization. The new combination of QPSO-FLSM algorithm iteratively optimizes initial contours using the QPSO method and fuzzy c-means clustering, and then utilizes level set method (LSM) to segment images. The new algorithm exploits the global search capability of QPSO to obtain a stable cluster center and a pre-segmentation contour closer to the region of interest during the iteration. In the implementation of the new method in segmenting liver tumors, brain tissues, and lightning images, the fitness function of the objective function of QPSO-FLSM algorithm is optimized by 10% in comparison to the original FLSM algorithm. The achieved initial contours from the QPSO-FLSM algorithm are also more stable than that from the FLSM. The QPSO-FLSM resulted in improved final image segmentation.

引用

页码：1065 / 1072

页数：8

共 50 条

[31] A Novel Level Set Method with Improved Fuzzy C-Means Based on Genetic Algorithm for Image Segmentation
Li, Dengao
Han, Zhe
Zhao, Jumin
2017 3RD INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING AND COMMUNICATIONS (BIGCOM), 2017, : 151 - 157
[32] An Adaptive Image Segmentation Method Based on the Level Set
Zhang Aili
Li Sijia
Liu Tuanning
Li Zhiyong
Zhang Yu
PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON MATERIALS ENGINEERING AND INFORMATION TECHNOLOGY APPLICATIONS, 2015, 28 : 496 - 502
[33] A Study of Image Segmentation Based on Level Set Method
Chang, Xiaowei
Li, Daowang
Xu, Yihong
PROCEEDINGS OF THE 2013 INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER SCIENCE AND ELECTRONICS INFORMATION (ICACSEI 2013), 2013, 41 : 360 - 363
[34] The cell image segmentation based on level set method
Li, Yibing
Zhu, Yao
Ye, Fang
Journal of Computational Information Systems, 2013, 9 (21): : 8467 - 8474
[35] Hybrid level set method based on image diffusion
Wang, Xiao-Feng
Zou, Le
Xu, Li-Xiang
Lv, Gang
Tang, Chao
NEUROCOMPUTING, 2017, 228 : 53 - 64
[36] Makers based level set method for image segmentation
Zheng, Sheng
Yang, Chang-Cai
Xiang, Shi-Ling
Ye, Jin
PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2008, : 947 - +
[37] MRI SEGMENTATION BY FUZZY CLUSTERING METHOD BASED ON PRIOR KNOWLEDGE
Yazdi, Mahsa Badiee
Khalilzadeh, Mohammad Mahdi
Foroughipour, Mohsen
BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS, 2016, 28 (04):
[38] Approach of the TWB Partition Based on Level Set Method
Liu, Yushan
Xu, Anping
2014 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (IEEE ICMA 2014), 2014, : 2044 - 2048
[39] Integrating clustering with level set method for piecewise constant Mumford-Shah model
Chen, Qiang
He, Chuanjiang
EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING, 2014,
[40] Integrating clustering with level set method for piecewise constant Mumford-Shah model
Qiang Chen
Chuanjiang He
EURASIP Journal on Image and Video Processing, 2014

← 1 2 3 4 5 →