The kidney tissue image is affected by other interferences in the tissue, which makes it difficult to extract the kidney tissue image features, and it is difficult to judge the lesion characteristics and types by intelligent feature recognition. In order to improve the efficiency and accuracy of feature extraction of kidney tissue images, refer to the ultrasonic heart image for analysis and then apply it to the feature extraction of kidney tissue. This paper proposes a feature extraction method based on ultrasound image segmentation. Moreover, this study combines the optical flow method and the speckle tracking algorithm to select the best image tracking method and optimizes the algorithm speed through the full search method and the two-dimensional log search method. In addition, this study verifies the performance of the method proposed in this paper through comparative experimental research, and this study combines statistical analysis methods to perform data analysis. The research results show that the algorithm proposed in this paper has a certain effect.
机构:
Beijing Univ Chinese Med, Affiliated Hosp 3, Spin Dept, Beijing 100029, Peoples R ChinaBeijing Univ Chinese Med, Affiliated Hosp 3, Spin Dept, Beijing 100029, Peoples R China
Zhang, Qingfeng
Du, Yun
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Beijing Univ Chinese Med, Sch Clin Med 2, Beijing 100078, Peoples R ChinaBeijing Univ Chinese Med, Affiliated Hosp 3, Spin Dept, Beijing 100029, Peoples R China
Du, Yun
Wei, Zhiqiang
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Beijing Univ Chinese Med, Dongfang Hosp, Orthopaed, Beijing 100078, Peoples R ChinaBeijing Univ Chinese Med, Affiliated Hosp 3, Spin Dept, Beijing 100029, Peoples R China
Wei, Zhiqiang
Liu, Hengping
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Beijing Univ Chinese Med, Affiliated Hosp 3, Spin Dept, Beijing 100029, Peoples R ChinaBeijing Univ Chinese Med, Affiliated Hosp 3, Spin Dept, Beijing 100029, Peoples R China
Liu, Hengping
Yang, Xiaoxia
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Beijing Univ Chinese Med, Affiliated Hosp 3, Spin Dept, Beijing 100029, Peoples R ChinaBeijing Univ Chinese Med, Affiliated Hosp 3, Spin Dept, Beijing 100029, Peoples R China
Yang, Xiaoxia
Zhao, Dongfang
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Beijing Univ Chinese Med, Dongfang Hosp, Orthopaed, Beijing 100078, Peoples R ChinaBeijing Univ Chinese Med, Affiliated Hosp 3, Spin Dept, Beijing 100029, Peoples R China