An Optimized Level Set Method Based on QPSO and Fuzzy Clustering

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

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.5   pp.1065-1072
Publication Date: 2019/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDP7132
Type of Manuscript: PAPER
Category: Image Processing and Video Processing
image segmentation,  fuzzy c-means clustering method (FCM),  level set method (LSM),  quantum particle swarm optimization (QPSO),  QPSO-FLSM method,  

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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.