[1]朱佳琦,刘翔,宋家琳.全局与局部特征结合的肝硬化病程分类方法[J].中国医学物理学杂志,2023,40(11):1362-1369.[doi:DOI:10.3969/j.issn.1005-202X.2023.11.008]
　ZHU Jiaqi,LIU Xiang,SONG Jialin.Classification of liver cirrhosis using global and local features[J].Chinese Journal of Medical Physics,2023,40(11):1362-1369.[doi:DOI:10.3969/j.issn.1005-202X.2023.11.008]
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全局与局部特征结合的肝硬化病程分类方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第11期
页码:
1362-1369
栏目:
医学影像物理
出版日期:
2023-11-24

文章信息/Info

Title:
Classification of liver cirrhosis using global and local features
文章编号:
1005-202X（2023）11-1362-08
作者:
朱佳琦1; 刘翔1; 宋家琳2
1.上海工程技术大学电子电气工程学院， 上海 201620； 2.中国人民解放军第二军医大学长征医院超声诊疗科， 上海 200003
Author(s):
ZHU Jiaqi1;  LIU Xiang1;  SONG Jialin2
1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China 2. Department of Ultrasound Diagnosis and Treatment, Shanghai Changzheng Hospital, Naval Medical University, Shanghai 200003, China
关键词:
肝硬化; 计算机辅助诊断; 超声图像处理; 特征分析
Keywords:
Keywords: liver cirrhosis computer-aided diagnosis ultrasound image processing feature analysis
分类号:
R318；R657.31
DOI:
DOI:10.3969/j.issn.1005-202X.2023.11.008
文献标志码:
A
摘要:
目的：提出一种肝脏组织全局特征与局部特征结合的肝硬化病程分类方法，用于解决目前肝硬化诊断主要依赖于人工检测的问题。方法：收集47例乙型肝炎肝硬化患者（依据Child-Pugh改良分级标准分为轻度肝硬化组、中度肝硬化组、重度肝硬化组）及20名健康志愿者（正常对照组）的二维高频超声图像，提出差分滤波器方法提取图像的码距，并提取角点、条索、腹水等特征进行定量分析。建立两阶段分类模型，第一阶段采用RBF核函数的SVM，将轻度与中度肝硬化归为一组，正常与重度肝硬化归为一组进行分类，避免两组之间的互相干扰。第二阶段采用贝叶斯对RF进行参数调优，调整不同特征的重要性权重，对每组的两个类别分别分类，提升肝硬化病程分类的准确率。结果：提出的特征均具有统计学意义（P<0.05），采用SVM-RF模型对正常对照组、轻度肝硬化组、中度肝硬化组、重度肝硬化组4个阶段的最终分类准确率分别达到93.11%、88.19%、91.93%和96.86%。结论：本文方法可以有效提取符合医生视角的全局特征以及局部特征，辅助诊断肝硬化病程。
Abstract:
Abstract: Objective To propose a classification method for liver cirrhosis diagnosis using both global and local features of liver tissues, thereby overcoming the current problem that the diagnosis of liver cirrhosis mainly relies on manual detection. Methods The two-dimensional high-frequency ultrasound images were collected from 47 patients with hepatitis B cirrhosis (divided into mild, moderate and severe cirrhosis groups according to Child-Pugh score) and 20 healthy volunteers (normal control group). The differential filter was used to obtain the code distance of the image, and then detect the corner point, cord, ascites and other features for quantitative analysis. A two-stage classification model was established. The support vector machine (SVM) of radial basis function was used in the first stage of classification to group mild and moderate cirrhosis into one category, while normal controls and severe cirrhosis into the other category for avoiding mutual interference between the two categories. In the second stage, after parameters tuning with Bayesian, random forest (RF) algorithm was used to adjust the importance weights of different features for further enhancing the classification accuracy of liver cirrhosis. Results All of the selected features were statistically significant (P<0.05), and the SVM-RF model had an accuracy of 93.11%, 88.19%, 91.93% and 96.86% for diagnosing normal liver, mildly cirrhosis liver, moderate cirrhosis liver and severe cirrhosis liver. Conclusion The proposed SVM-RF method can effectively extract the global and local features that conform to the doctors perspective, and assist in cirrhosis diagnosis.

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备注/Memo

备注/Memo:
【收稿日期】2023-07-05 【基金项目】上海市自然科学基金（19ZR1421500） 【作者简介】朱佳琦，硕士研究生，研究方向：医学图像处理，E-mail: 1481784173@qq.com 【通信作者】刘翔，博士，教授，研究生导师，研究方向：计算机视觉与人工生命，E-mail: xliu@sues.edu.cn
更新日期/Last Update: 2023-11-24