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基于超声图像分割的肾脏组织图像特征提取。

Feature Extraction of Kidney Tissue Image Based on Ultrasound Image Segmentation.

机构信息

Department of Ultrasound, Harbin Medical University Fourth Hospital, Harbin 150001, Heilongjiang, China.

Department of Cardiology, Harbin Medical University Fourth Hospital, Harbin 150001, Heilongjiang, China.

出版信息

J Healthc Eng. 2021 Apr 26;2021:9915697. doi: 10.1155/2021/9915697. eCollection 2021.

DOI:10.1155/2021/9915697
PMID:33986943
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8093061/
Abstract

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.

摘要

肾脏组织图像受到组织内其他干扰的影响，使得肾脏组织图像特征难以提取，智能特征识别也难以判断病变特征和类型。为了提高肾脏组织图像特征提取的效率和准确性，本文参考超声心动图像进行分析，然后将其应用于肾脏组织的特征提取。本文提出了一种基于超声图像分割的特征提取方法。此外，本研究结合光流法和散斑跟踪算法，选择最佳的图像跟踪方法，并通过全搜索法和二维对数搜索法优化算法速度。此外，本研究通过对比实验研究验证了本文提出的方法的性能，并结合统计分析方法进行数据分析。研究结果表明，本文提出的算法具有一定的效果。

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