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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
영상 분할의 안정성과 정밀도를 향상시키고 초기화 민감도를 줄이기 위해 QPSO(양자 입자 군집 최적화)의 전역 검색 기능을 기반으로 하는 새로운 퍼지 수준 설정 방법(FLSM)이 제안되었습니다. QPSO-FLSM 알고리즘의 새로운 조합은 QPSO 방법과 퍼지 c-평균 클러스터링을 사용하여 초기 윤곽을 반복적으로 최적화한 다음 LSM(레벨 설정 방법)을 활용하여 이미지를 분할합니다. 새로운 알고리즘은 QPSO의 전역 검색 기능을 활용하여 반복 중에 관심 영역에 더 가까운 안정적인 클러스터 중심과 사전 분할 윤곽선을 얻습니다. 간 종양, 뇌 조직 및 번개 영상을 분할하는 새로운 방법의 구현에서 QPSO-FLSM 알고리즘의 목적 함수의 적합도는 원래 FLSM 알고리즘에 비해 10% 최적화되었습니다. QPSO-FLSM 알고리즘에서 얻은 초기 윤곽선도 FLSM의 윤곽선보다 더 안정적입니다. QPSO-FLSM을 사용하면 최종 이미지 분할이 향상되었습니다.
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
부
@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},}
부
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 -