一种基于LBP特征提取和稀疏表示的肝病识别算法

doi:10.3969/j.issn.0258-8021.2017.06.002

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Abstract Because of the existence of the uneven echo, the dim edge and other factors under the ultrasonic environment, the diagnose accuracy of liver diseases can be influenced. Besides, the clinical diagnosis for liver diseases based on liver ultrasonic figure is mainly based on the conventional visual assessment that relies on the subjective experience of radiologists, leading to inaccurate and inadequate results. This study therefore proposed alocal binary pattern feature extraction method combining sparse representation algorithm. The proposed method extracted theregions of interest from liver ultrasonic figures, LBP method for feature extraction, dictionary learning method for training, andsupport vector machine for classification. In order to verify this approach, experiments were carried out by selecting samples from department of hepatobiliary surgery of affiliated hospital of Qingdao University. Results showed in the first experiment that the accuracy rate reached 99.50% of 100 normal liver samples and 100 liver cirrhosis samples. And results showed in the second experiment that the AUC of liver cirrhosis, fatty liver, hemangioma and liver cancer reached 67.2%, 65.1%, 55.0% and 62.6% separately of total 200 samples. The comparison between the proposed method and conventional methods, via receiver operating characteristic curve, demonstrated that the proposed method possessed the advantages both in accuracy and generalization performance. The proposed method would be helpful for clinical diagnosis of liver diseases.

Key words： liver ultrasonic figure LBP feature sparse representation liver diseases recognition ROC curve

Received: 23 December 2016

PACS:

R318

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	Articles by authors
	Han Xiuzhi
	Zhao Ximei
	Yu Kexin
	Wang Guodong

Cite this article:

Han Xiuzhi,Zhao Ximei,Yu Kexin, et al. An Algorithm of the LBP Feature Extraction Method Combining Sparse Representation in Liver Diseases Recognition[J]. Chinese Journal of Biomedical Engineering, 2017, 36(6): 647-653.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2017.06.002 OR http://cjbme.csbme.org/EN/Y2017/V36/I6/647

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