针对肝包膜特征图的自动化提取方法

doi:10.16180/j.cnki.issn1007-7820.2022.10.007

Abstract

Abstract:

In order to automatically extract the feature maps of the liver capsule and its upper and lower tissues, and realize automatic feature learning, the study proposes to use frequency domain processing and image morphology processing to preprocess the image, and proposes a two-way cross receptive field strategy based on the moving average method, and feature screening and analysis are carried out through the receptive field mapping area. The logarithmic energy function is used to identify and locate the target block, so as to realize the extraction and analysis of the liver parenchymal lesion features, liver capsule, muscle fat layer texture feature data, and obtain the liver capsule and its upper and lower tissue feature maps according to the data analysis. Based on the relative positions of the proposed feature regions, a block correction mechanism is proposed to correct the mischecked blocks to make them more robust. The experiments show that during the extraction of the liver envelope and its upper and lower tissue feature maps in the ultrasound images of cirrhosis, the present extraction mechanism achieves 100% accuracy in the normal, mild and moderate stages of feature extraction, and 84.6% accuracy is achieved in the severe disease stage.

Key words: high-frequency ultrasound images, cirrhosis, Fourier transform, liver capsule, two-way cross-receptor field, logarithmic energy gain function, moving average method, error correction mechanism

CLC Number:

TN911.73

NIU Guangli,LIU Xiang,SONG Jialin,TANG Xian. Automated Extraction Method for Liver Capsule Feature Maps[J].Electronic Science and Technology, 2022, 35(10): 39-44.

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References 18

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组别	样本总数	提取正确数	提取正确率	平均准确率
正常组	20	20	100.00%	96.15%
轻度组	18	18	100.00%
中度组	17	17	100.00%
重度组	13	11	84.60%

Automated Extraction Method for Liver Capsule Feature Maps

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