基于双侧TIC定量特征的乳腺肿瘤良恶性鉴别

doi:10.12068/j.issn.1005-3026.2019.04.004

东北大学学报:自然科学版 ›› 2019, Vol. 40 ›› Issue (4): 473-477.DOI: 10.12068/j.issn.1005-3026.2019.04.004

基于双侧TIC定量特征的乳腺肿瘤良恶性鉴别

孙航¹，李宏¹，刘思琪¹，张伟²

(1. 东北大学中荷生物医学与信息工程学院，辽宁沈阳110169; 2. 中国医科大学附属盛京医院，辽宁沈阳110004)

收稿日期:2018-03-07 修回日期:2018-03-07 出版日期:2019-04-15 发布日期:2019-04-26
通讯作者: 孙航
作者简介:孙航(1985-)，女，辽宁沈阳人，东北大学讲师，博士研究生.
基金资助:
国家重点研发项目子课题(2016YFC1303005).

Breast Tumor Classification Based on Bilateral TIC Quantitative Features

SUN Hang¹， LI Hong¹， LIU Si-qi¹， ZHANG Wei²

1. School of Sino-Dutch Biomedical & Information Engineering， Northeastern University， Shenyang 110169， China; 2. Shengjing Hospital，China Medical University， Shenyang 110004， China.

Received:2018-03-07 Revised:2018-03-07 Online:2019-04-15 Published:2019-04-26
Contact: ZHANG Wei
About author:-
Supported by:
-

摘要/Abstract

摘要： 基于DCE-MRI提出了一种利用双侧乳腺对称区域的TIC定量特征识别乳腺肿瘤良恶性的方法.使用三维区域生长算法提取乳腺的病灶区，基于病灶区及其对侧乳腺对应的ROI的TIC曲线分别提取29个特征，并定义双侧差异特征参数，经SFFS方法筛选后得到7个有效特征.使用SVM进行特征训练，基于交叉验证方法得到分类结果.本研究随机选取回顾性病例112例(良性67例，恶性45例)，得到肿瘤良恶性平均分类准确率为88.39%.实验结果表明:此方法对乳腺肿瘤的良恶性鉴别有较高的准确率，对辅助医生进行乳腺病变组织的良恶性鉴别具有重要价值.

关键词: 乳腺肿瘤, DCE-MRI, TIC曲线, 双侧定量分析, 良恶性鉴别

Abstract: An effective bilateral quantitative analyzing method for breast tumor classification was proposed based on the time intensity curve(TIC) of dynamic contrast enhanced magnetic resonance imaging(DCE-MRI). The breast lesion region in DCE-MRI images was extracted using three-dimensional region growing algorithm， and 29 features were extracted based on the TIC curves of the ROI corresponding to the lesion area and its contralateral mammary gland. The two-sided difference feature parameters were defined， and 7 effective features after screening by the sequential floating forward selection(SFFS) method were obtained. The support vector machines(SVM) was used for classification， and the classification results were obtained on the basis of cross-validation method for feature training. A hundred and twelve retrospective cases(67 benign， 45 malignant) were chosen randomly， and the average classification accuracy is 88.39%.The experimental results showed that this method has a high accuracy rate for breast tumor classification， and is of great value for assisting doctors in differential diagnosis of breast tumor.

Key words: breast tumor, DCE-MRI, TIC curve, bilateral quantitative analysis, tumor classification

中图分类号:

TP391

孙航，李宏，刘思琪，张伟. 基于双侧TIC定量特征的乳腺肿瘤良恶性鉴别[J]. 东北大学学报:自然科学版, 2019, 40(4): 473-477.

SUN Hang， LI Hong， LIU Si-qi， ZHANG Wei. Breast Tumor Classification Based on Bilateral TIC Quantitative Features[J]. Journal of Northeastern University Natural Science, 2019, 40(4): 473-477.

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基于双侧TIC定量特征的乳腺肿瘤良恶性鉴别

Breast Tumor Classification Based on Bilateral TIC Quantitative Features

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