Journal of Jianghan University (Natural Science Edition) ›› 2020, Vol. 48 ›› Issue (6): 78-83.doi: 10.16389/j.cnki.cn42-1737/n.2020.06.011

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Evolutionary Region Growing Segmentation Algorithm Based on Maximum Entropy

WEI Guangxing1，ZHOU Xianzhong2，LI Hua1   

  1. 1. School of Information Engineering，Chuzhou Polytechnic，Chuzhou 239000，Anhui，China；2. School of Management and Engineering，Nanjing University，Nanjing 210093，Jiangsu，China
  • Online:2020-12-28 Published:2020-12-18
  • Supported by:
    国家自然科学基金资助项目（71671086）；安徽省质量工程项目（2019kfkc227）；滁州职业技术学院校级质量工程项目（2018sjjd002，2017zlgc008，2017zlgc044）

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Abstract: Region growing segmentation is a fast and easy algorithm for image segmentation，but it is sensitive to the number of initialization points. An evolutionary region growing segmentation algorithm based on maximum entropy is proposed. The principle of evolutionary maximum entropy is used to calculate the number of regions that are segmented by the evolutionary region. The population is generated according to the number of regions，and the optimal seed and fitness value in each population is calculated and obtained. And then the segmentation of the image is realized. Experimental results show that the algorithm can obtain the image segmentation results more accurately.

Key words: maximum entropy, region growing, image segmentation

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