[1]申楠,邢素霞,何湘萍,等.基于Adaboost-决策树算法的乳腺微钙化区域真假阳性检测[J].中国医学物理学杂志,2021,38(8):940-945.[doi:DOI:10.3969/j.issn.1005-202X.2021.08.004]
　SHEN Nan,XING Suxia,HE Xiangping,et al.True- and false-positive detections of breast microcalcifications based on Adaboost-decision tree algorithm[J].Chinese Journal of Medical Physics,2021,38(8):940-945.[doi:DOI:10.3969/j.issn.1005-202X.2021.08.004]
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基于Adaboost-决策树算法的乳腺微钙化区域真假阳性检测()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第8期
页码:
940-945
栏目:
医学影像物理
出版日期:
2021-08-02

文章信息/Info

Title:
True- and false-positive detections of breast microcalcifications based on Adaboost-decision tree algorithm
文章编号:
1005-202X（2021）08-0940-06
作者:
申楠1; 邢素霞1; 何湘萍2; 潘子妍1; 王瑜1
1.北京工商大学人工智能学院， 北京 100048； 2.北京海淀妇幼保健院乳腺病防治中心， 北京 100080
Author(s):
SHEN Nan1;  XING Suxia1;  HE Xiangping2;  PAN Ziyan1;  WANG Yu1
1. School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China 2. Breast Disease Prevention and Control Center, Haidian Maternal and Child Health Hospital, Beijing 100080, China
关键词:
乳腺癌; Adaboost-决策树; 微钙化; Haralick纹理特征; 灰度游程矩阵
Keywords:
Keywords: breast cancer Adaboost-decision tree microcalcification Haralick texture feature grey-level run length matrix
分类号:
R318；TP301.6
DOI:
DOI:10.3969/j.issn.1005-202X.2021.08.004
文献标志码:
A
摘要:
乳腺癌的早期症状在乳腺钼靶图像中主要表现为微钙化点，微钙化区域的真假阳性检测对于乳腺癌早期筛查具有重要意义。本研究选取DDSM图像进行实验，手动截取了400个疑似钙化区域。首先提取全部区域的Haralick纹理特征和灰度游程矩阵特征建立特征集，然后使用Adaboost算法集成决策树，构建强分类器AB-DT，对400个疑似钙化区域进行分类。实验发现当集成462棵决策树时，模型分类性能最佳。最后进行10折交叉验证，AB-DT算法达到了91.75%的准确率，91.75%的敏感性，91.79%的特异性，F1指数为0.918 7。该模型在微钙化真假阳性检测上性能优越，可用于辅助乳腺微钙化点检测，具有一定的临床应用价值。
Abstract:
Abstract: The early manifestation of breast cancer is mainly characterized by microcalcifications in mammograms. The true- and false-positive detections of microcalcifications are of great significance for the early screening of breast cancer. DDSM images were selected for the experiment, and 400 suspected calcification regions were manually intercepted. The feature set was firstly established by extracting Haralick texture features and grey-level run length matrix features of all regions and then, Adaboost algorithm was integrated with decision tree to construct a strong classifier AB-DT for classifying 400 suspected calcification regions. It was found that the model classification performance was the best when 462 decision trees were integrated. Finally, 10-fold cross-validation was conducted, and the results revealed that the accuracy, sensitivity and specificity of AB-DT algorithm reached 91.75%, 91.75% and 91.79%, respectively, and that F1 score was 0.918 7. The proposed model has superior performance in the true- and false-positive detections of microcalcifications, and it can be used to assist the detection of breast microcalcifications, which has certain clinical application value.

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备注/Memo

备注/Memo:
【收稿日期】2021-03-18 【基金项目】国家自然科学基金（61671028）；国家重大科学研发子课题（ZLJC6 03-5-1） 【作者简介】申楠，在读研究生，主要从事图像处理、机器学习方面的研究，E-mail: 18210629776@163.com 【通信作者】邢素霞，博士，副教授，主要从事图像处理与嵌入式系统的研究，E-mail: xingsuxia@163.com
更新日期/Last Update: 2021-07-30