An Optimized Level Set Method Based on QPSO and Fuzzy Clustering

Ling YANG; Yuanqi FU; Zhongke WANG; Xiaoqiong ZHEN; Zhipeng YANG; Xingang FAN

doi:10.1587/transinf.2018EDP7132

An Optimized Level Set Method Based on QPSO and Fuzzy Clustering

Ling YANG, Yuanqi FU, Zhongke WANG, Xiaoqiong ZHEN, Zhipeng YANG, Xingang FAN

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Summary :

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.

Publication: IEICE TRANSACTIONS on Information Vol.E102-D No.5 pp.1065-1072

Publication Date: 2019/05/01

Publicized: 2019/02/12

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2018EDP7132

Type of Manuscript: PAPER

Category: Image Processing and Video Processing

Authors

Ling YANG
  Chengdu University of Information Technology
Yuanqi FU
  Chengdu University of Information Technology
Zhongke WANG
  Chengdu University of Information Technology
Xiaoqiong ZHEN
  Chengdu University of Information Technology
Zhipeng YANG
  Chengdu University of Information Technology
Xingang FAN
  Western Kentucky University

Keyword

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

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Ling YANG, Yuanqi FU, Zhongke WANG, Xiaoqiong ZHEN, Zhipeng YANG, Xingang FAN, "An Optimized Level Set Method Based on QPSO and Fuzzy Clustering" in IEICE TRANSACTIONS on Information, vol. E102-D, no. 5, pp. 1065-1072, May 2019, doi: 10.1587/transinf.2018EDP7132.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7132/_p

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@ARTICLE{e102-d_5_1065,
author={Ling YANG, Yuanqi FU, Zhongke WANG, Xiaoqiong ZHEN, Zhipeng YANG, Xingang FAN, },
journal={IEICE TRANSACTIONS on Information},
title={An Optimized Level Set Method Based on QPSO and Fuzzy Clustering},
year={2019},
volume={E102-D},
number={5},
pages={1065-1072},
abstract={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.},
keywords={},
doi={10.1587/transinf.2018EDP7132},
ISSN={1745-1361},
month={May},}

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TY - JOUR
TI - An Optimized Level Set Method Based on QPSO and Fuzzy Clustering
T2 - IEICE TRANSACTIONS on Information
SP - 1065
EP - 1072
AU - Ling YANG
AU - Yuanqi FU
AU - Zhongke WANG
AU - Xiaoqiong ZHEN
AU - Zhipeng YANG
AU - Xingang FAN
PY - 2019
DO - 10.1587/transinf.2018EDP7132
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2019
AB - 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.
ER -