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中国医疗器械杂志 2020, Vol. 44 Issue (4) :294-301    DOI: 10.3969/j.issn.1671-7104.2020.04.003
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乳腺肿瘤超声图像的多特征提取及分类研究
任丽1,刘洋洋2,童莹2,曹雪虹1, 2,吴意贇3
1 南京邮电大学 通信与信息工程学院,南京市,210003
2 南京工程学院 信息与通信工程学院,南京市,211167
3 南京中医药大学,南京市,210029
Multi-feature Extraction and Classification of Breast Tumor in Ultrasound Image
REN Li1, LIU Yangyang2, TONG Ying2, CAO Xuehong1, 2, WU Yiyun3
1 School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications,
Nanjing, 210003
2 School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, 211167
3 Nanjing University of Chinese Medicine, Nanjing, 210029

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摘要 目的 乳腺肿瘤特征提取是超声乳腺肿瘤良恶性检测的重要部分,针对传统超声乳腺肿瘤良恶性量化特征 描述存在不准确等缺点,研究了一种简单、准确的特征提取方法。 方法 提出一种新的边界特征提取方 法,首先构造超声乳腺肿瘤的形状直方图,然后从局部的角度计算相关边界特征因子:最大曲率和、最 大曲率峰值和、最大曲率标准差和;基于边界特征、形状特征和纹理特征构建线性支持向量机(support vector machine, SVM)分类器,用于乳腺肿瘤良恶性判别。结果 边界特征判断良恶性乳腺肿瘤的准确 率为82.69%,形状特征为73.08%,纹理特征为63.46%,多特征(边界特征、形状特征和纹理特征)为 86.54%。结论 边界特征相对于纹理特征和形状特征具有较高的分类准确性,结合三类特征的识别准确率 最高,从多角度描述肿瘤良恶性,研究结果具有实用价值。
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关键词乳腺肿瘤   边界特征   形状特征   纹理特征   分类     
Abstract: Objective Feature extraction of breast tumors is very important in the breast tumor detection (benign and malignant) in ultrasound image. The traditional quantitative description of breast tumors has some shortcomings, such as inaccuracy. A simple and accurate feature extraction method has been studied. Methods In this paper, a new method of boundary feature extraction was proposed. Firstly, the shape histogram of ultrasound breast tumors was constructed. Secondly, the relevant boundary feature factors were calculated from a local point of view, including sum of maximum curvature, sum of maximum curvature and peak, sum of maximum curvature and standard deviation. Based on the boundary features, shape features and texture features, the linear support vector machine classifiers for benign and malignant breast tumor recognition was constructed. Results The accuracy of boundary features in the benign and malignant breast tumors classification was 82.69%. The accuracy of shape features was 73.08%. The accuracy of texture features was 63.46%. The classification accuracy of the three fusion features was 86.54%. Conclusion The classification accuracy of boundary features was higher than that of texture features and shape features. The classification method based on multifeatures has the highest accuracy and it describes the benign and malignant tumors from different angles. The research results have practical value.
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Received 2019-10-30;
Fund:

南京工程学院高层次引进人才科研启动基金(YKJ201862);国家自然科学基金(61703201);江苏省自然科学基金(BK20170765);
南京工程学院青年创新基金(CKJB201602)

Corresponding Authors: 曹雪虹     Email: caoxh@njupt.edu.cn
About author: 任丽,E-mail: 961354101@qq.com
引用本文:   
任丽1,刘洋洋2,童莹2,曹雪虹1, 2,吴意贇3.乳腺肿瘤超声图像的多特征提取及分类研究[J]  中国医疗器械杂志, 2020,V44(4): 294-301
REN Li1, LIU Yangyang2, TONG Ying2, CAO Xuehong1, 2, WU Yiyun3.Multi-feature Extraction and Classification of Breast Tumor in Ultrasound Image[J]  Chinese Journal of Medical Instrumentation, 2020,V44(4): 294-301
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