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

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