An Optimized Level Set Method Based on QPSO and Fuzzy Clustering

DOI Web Site 参考文献22件
  • YANG Ling
    Electronic Engineering College, Chengdu University of Information Technology
  • FU Yuanqi
    Electronic Engineering College, Chengdu University of Information Technology
  • WANG Zhongke
    Information Security Engineering College, Chengdu University of Information Technology
  • ZHEN Xiaoqiong
    Electronic Engineering College, Chengdu University of Information Technology
  • YANG Zhipeng
    Electronic Engineering College, Chengdu University of Information Technology
  • FAN Xingang
    Department of Geography and Geology, Western Kentucky University

抄録

<p>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.</p>

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