Multimedia Tools and Applications

, Volume 76, Issue 6, pp 8951–8968 | Cite as

Segmentation of liver cyst in ultrasound image based on adaptive threshold algorithm and particle swarm optimization

  • Haijiang Zhu
  • Zhanhong Zhuang
  • Jinglin Zhou
  • Fan Zhang
  • Xuejing Wang
  • Yihong Wu
Article
  • 220 Downloads

Abstract

To find the optimum threshold of an image is still an important research topic in the recent years. This paper presents a segmentation of liver cyst for ultrasound image through combining Wellner’s thresholding algorithm with particle swarm optimization (PSO). The proposed method firstly obtains an optimal parameter, which expressed as a percentage or fixed amount of dark objects against a white background in a gray image, of Wellner’s thresholding algorithm by PSO method. And then the gray image is binarized according to the optimized parameter. Finally, a semi-automatic method for locating and identifying multiple liver cysts or single liver cyst of ultrasound images is performed. For a validation, the results of the proposed technique are compared with those of other segmented methods. We also tested 92 ultrasound images of the liver cysts by our software. The corrected identification rate of the single liver cysts is 97.7 %, and that of multiple liver cysts is 87.5 %. Experimental results demonstrate that the proposed technique is reliable on segmenting the contour of liver cyst and identifying single or multiple liver cysts.

Keywords

Ultrasound image Wellner’s thresholding algorithm Particle swarm optimization Segmentation of liver cyst 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Haijiang Zhu
    • 1
  • Zhanhong Zhuang
    • 1
  • Jinglin Zhou
    • 1
  • Fan Zhang
    • 1
  • Xuejing Wang
    • 1
  • Yihong Wu
    • 2
  1. 1.College of Information & TechnologyBeijing University of Chemical TechnologyBeijingChina
  2. 2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of SciencesBeijingChina

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