[1]吴秀明,王霞丽,吕国荣,等.计算机辅助系统在诊断乳腺良恶性肿瘤中的应用[J].中国医学物理学杂志,2020,37(3):374-378.[doi:DOI:10.3969/j.issn.1005-202X.2020.03.022]
　WU Xiuming,WANG Xiali,LYU Guorong,et al.Application of computer-aided detection in ultrasound diagnosis of benign and malignant breast tumors[J].Chinese Journal of Medical Physics,2020,37(3):374-378.[doi:DOI:10.3969/j.issn.1005-202X.2020.03.022]
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计算机辅助系统在诊断乳腺良恶性肿瘤中的应用()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第3期
页码:
374-378
栏目:
其他（激光医学等）
出版日期:
2020-03-25

文章信息/Info

Title:
Application of computer-aided detection in ultrasound diagnosis of benign and malignant breast tumors
文章编号:
1005-202X（2020）03-0374-05
作者:
吴秀明1; 王霞丽2; 吕国荣2; 魏梦婉3; 杜永兆2; 3; 柳培忠2; 3
1.福建医科大学附属泉州第一医院超声科， 福建 泉州 362000； 2.泉州医学高等专科学校省级母婴健康服务应用技术协同创新 中心， 福建 泉州 362000； 3.华侨大学工学院，福建 泉州 362000
Author(s):
WU Xiuming1;  WANG Xiali2;  LYU Guorong2;  WEI Mengwan3;  DU Yongzhao2;  3;  LIU Peizhong2;  3
1. Department of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China; 2. Collaborative Innovation Center for Maternal and Infant Health Service Application Technology, Quanzhou Medical College, Quanzhou 362000, China; 3. Engineering Institute, Huaqiao University, Quanzhou 362000, China
关键词:
计算机辅助诊断; 超声; 乳腺肿瘤; 特征提取; SVM分类器
Keywords:
Keywords: computer-aided detection;  ultrasound;  breast tumor;  feature extraction;  support vector machine classifier
分类号:
R319；R737.9
DOI:
DOI:10.3969/j.issn.1005-202X.2020.03.022
文献标志码:
A
摘要:
目的：探讨计算机辅助诊断系统在良恶性肿瘤检测与特征提取基础上的分类对于乳腺肿瘤的诊断价值。方法：回顾性分析乳腺超声检查发现肿瘤且经过病理学证实的617例患者影像资料，采用手工提取的方式得到乳腺超声图像的感兴趣区域及病灶轮廓，再利用方向梯度直方图（HOG）、局部二值模式（LBP）和灰度共生矩阵（GLCM）3个特征进行乳腺肿瘤的良恶性病变真假阳性检测；最后用受试者操作特征曲线（ROC）分别分析每个特征对于两类病变判别的诊断性能和应用所有特征集合的分类诊断性能。结果：多特征融合方法的各项诊断效能及ROC曲线下面积（AUC）值均优于单特征LBP、HOG、GLCM（P值均<0.05）。与人工诊断相比，多特征融合的敏感性无显著差异，但特异度显著升高达98.57%（Z值=2.25, P<0.05），同时AUC值为0.985，显著优于人工诊断的0.910（Z值=1.99, P<0.05）。结论：计算机辅助系统乳腺超声肿瘤良恶性检测的算法是有效的，能够对乳腺癌鉴别诊断提供有益的参考。
Abstract:
Abstract: Objective To explore the diagnostic value of computer-aided detection in breast tumors based on the detection of benign and malignant tumors and feature extraction. Methods The ultrasound images of 617 patients with breast tumors detected by ultrasound and confirmed by pathology were analyzed retrospectively. The regions of interest and lesion contours in the breast ultrasound images were obtained by manual extraction, and then 3 features, namely histogram of oriented gradient (HOG), local binary pattern (LBP) and gray level co-occurrence matrix (GLCM), were used to detect the true or false positive of benign and malignant breast tumors. Finally, receiver operating characteristic (ROC) curve was used to analyze the diagnostic performance of each feature for two types of lesions, and the diagnostic performance of feature set for classification. Results The diagnostic performance and area under ROC curve (AUC) of the detection with the combination of multiple features were superior to those obtained by every single feature (LBP, HOG or GLCM) (all P<0.05). The detection with the combination of multiple features had a sensibility similar to that of manual diagnosis, and a significantly increased specificity which was up to 98.57% (Z value=2.25, P<0.05), and a AUC of 0.985 which was obviously higher than 0.910 of manual diagnosis (Z value=1.99, P<0.05). Conclusion The computer-aided detection for the ultrasound detection of benign and malignant breast tumors is proved to be effective and can provide useful reference for the differentiation and diagnosis of breast tumors.

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备注/Memo

备注/Memo:
【收稿日期】2019-12-11 【基金项目】国家自然科学基金（61605048）；福建省泉州市科技计划项目（2018N081S） 【作者简介】吴秀明，硕士，主治医师，主要研究方向：妇产超声和乳腺超声，E-mail: wxming1981@163.com 【通信作者】吕国荣，硕士，教授，主任医师，研究方向：产科超声和介入超声，E-mail: lgr_feus@sina.com
更新日期/Last Update: 2020-04-02