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The Recognition of Cirrhotic Liver Ultrasonic Images of Multi-feature Fusion Based on BP_Adaboost Neural Network

  • Shourun WangEmail author
  • Zhenkuan Pan
  • Weibo Wei
  • Ximei Zhao
  • Guodong Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 634)

Abstract

Due to the recognition rate of single character is low, the method of multi-feature fusion was proposed. BP_Adaboost neural network (BP_ANN) was first used to recognize the B-scan ultrasonic image of cirrhotic liver. Gray level co-occurrence matrix (GLCM) and gray level difference statistics (GLDS) were introduced in this paper. In order to improve the objectivity of the experimental results, uniform local binary pattern (U_LBP) was also applied. The texture features were extracted by any combination of these three methods. Then the feature which was extracted by above combination was input to BP_ANN. It was shown that the combination of GLCM and GLDS was better than any others in this experiment, and the recognition rate was 97 %. The design of BP_Adaboost network and the determination of neurons in hidden layer were also discussed.

Keywords

Neural network Texture feature Pattern recognition Cirrhotic liver Multi-feature fusion 

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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Shourun Wang
    • 1
    Email author
  • Zhenkuan Pan
    • 1
  • Weibo Wei
    • 1
  • Ximei Zhao
    • 1
  • Guodong Wang
    • 1
  1. 1.College of Computer Science and TechnologyQingdao UniversityQingdaoChina

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