The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Une nouvelle méthode d'ensemble de niveaux flous (FLSM) basée sur la capacité de recherche globale de l'optimisation par essaim de particules quantiques (QPSO) est proposée pour améliorer la stabilité et la précision de la segmentation d'image et réduire la sensibilité de l'initialisation. La nouvelle combinaison de l'algorithme QPSO-FLSM optimise de manière itérative les contours initiaux à l'aide de la méthode QPSO et du regroupement c-means flou, puis utilise la méthode d'ensemble de niveaux (LSM) pour segmenter les images. Le nouvel algorithme exploite la capacité de recherche globale de QPSO pour obtenir un centre de cluster stable et un contour de pré-segmentation plus proche de la région d'intérêt lors de l'itération. Dans la mise en œuvre de la nouvelle méthode de segmentation des tumeurs hépatiques, des tissus cérébraux et des images d'éclairs, la fonction de fitness de la fonction objectif de l'algorithme QPSO-FLSM est optimisée de 10 % par rapport à l'algorithme FLSM d'origine. Les contours initiaux obtenus à partir de l'algorithme QPSO-FLSM sont également plus stables que ceux du FLSM. Le QPSO-FLSM a permis d’améliorer la segmentation de l’image finale.
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
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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
Copier
@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},}
Copier
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 -