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.
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Wang, S., Pan, Z., Wei, W., Zhao, X., Wang, G. (2016). The Recognition of Cirrhotic Liver Ultrasonic Images of Multi-feature Fusion Based on BP_Adaboost Neural Network. In: Tan, T., et al. Advances in Image and Graphics Technologies. IGTA 2016. Communications in Computer and Information Science, vol 634. Springer, Singapore. https://doi.org/10.1007/978-981-10-2260-9_13
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DOI: https://doi.org/10.1007/978-981-10-2260-9_13
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