广西师范大学学报（自然科学版） ›› 2015, Vol. 33 ›› Issue (2): 22-28.doi: 10.16088/j.issn.1001-6600.2015.02.004

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基于小波变换与野草算法的细胞图像特征提取与识别

陈锦, 罗晓曙   

  1. 广西师范大学电子工程学院,广西桂林541004
  • 收稿日期:2015-01-06 出版日期:2015-02-10 发布日期:2018-09-20
  • 通讯作者: 罗晓曙(1972—),男,湖北应城人,广西师范大学教授,博士。E-mail: lxs@mailbox.gxnu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(21327007)

Extraction and Recognition of Cell Image Feature Basedon Wavelet Transform and Invasive Weed Optimization

CHEN Jin, LUO Xiao-shu   

  1. College of Electronic Engineering, Guangxi Normal University, Guilin Guangxi 541004,China
  • Received:2015-01-06 Online:2015-02-10 Published:2018-09-20

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摘要： 本文在细胞图像小波多尺度分解的基础上,提出在小波域中进行细胞图像特征提取的方法。针对基于小波变换提取的细胞图像特征向量维数过大、冗余等问题,提出一种基于小波变换与野草优化算法相结合的细胞图像特征的提取方法,最后利用BP神经网络作为分类器进行细胞图像识别。计算机实验仿真结果表明,与现有的未进行特征优化的细胞图像特征提取识别方法相比,本文细胞图像特征提取、分析、识别方法所需时间更短,识别正确率更高,实时性、鲁棒性能更好。

关键词: 小波多尺度分解, 野草优化算法, 特征向量, BP神经网络, 细胞图像识别

Abstract: A cell image feature extraction algorithm in wavelet domain based on multi-scale wavelet cell image decomposition is proplsed. According to the problem of the large and redundancy cell image feature vector dimension based on wavelet transform, a method of cell image feature extraction based on wavelet transform and the invasive weed optimization algorithm is put forwart. Finally, the BP neural network is used as the classifier for cell image recognition. Computer simulation results show that,compared with the existing cell image feature extraction and recognition method in no feature optimization, this algorithm of cell image feature extraction, analysis and recognition algorithm of need shorter time but with higher recognition rate, better real-time performance and better robust performance performance.

Key words: multi-scale wavelet decomposition, weed optimization algorithm, feature vector, BP neural network, cell image recognition

中图分类号: 

  • TP391.4
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