|本期目录/Table of Contents|

[1]章鸣嬛,顾雅佳,肖勤,等.基于坐标匹配和子图切分定位乳腺钼靶图像的感兴趣区域*[J].生物医学工程研究,2020,01:18-22.
 ZHANG Minghuan,GU Yajia,XIAO Qin,et al.Locating the region of interest within molybdenum target images based on coordinates matching and sub-image segmentation[J].Journal of Biomedical Engineering Research,2020,01:18-22.
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基于坐标匹配和子图切分定位乳腺钼靶图像的感兴趣区域*(PDF)

《生物医学工程研究》[ISSN:1006-6977/CN:61-1281/TN]

期数:
2020年01期
页码:
18-22
栏目:
出版日期:
2020-03-25

文章信息/Info

Title:
Locating the region of interest within molybdenum target images based on coordinates matching and sub-image segmentation
文章编号:
1672-6278 (2020)01-0018-05
作者:
章鸣嬛1顾雅佳2肖勤2刘文坚3张璇1陈瑛1△
1.上海杉达学院大数据分析与处理研究中心,上海 201209;2.复旦大学附属肿瘤医院放射诊断科,上海 200032;3.澳门城市大学人文社会科学学院,澳门 999078
Author(s):
ZHANG Minghuan1GU Yajia2XIAO Qin2LIU Wenjian3ZHANG Xuan1CHEN Ying1
1.Research center of Big Data Analyses and Process, Shanghai Sanda University, Shanghai 201209, China;2.Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032;3.Humanities and Social Sciences, City University of Macau, Macau 999078,China
关键词:
乳腺X线钼靶图像DDSM感兴趣区域坐标匹配模式识别
Keywords:
Mammography DDSMRegion of interest Coordinate matching Pattern recognition
分类号:
R318;Q334
DOI:
10.19529/j.cnki.1672-6278.2020.01.04
文献标识码:
A
摘要:
乳腺X线摄影技术是早期发现和诊断乳腺肿瘤的首选方法。提取乳腺钼靶图像的感兴趣区域(region of interest,ROI)并利用人工智能算法对其进行模式识别,可有效提高乳腺肿瘤筛查工作的效率。试验图像均来自DDSM乳腺X线钼靶图像公开数据库,以其中BI-RADS分类为第4类(BI-RADS4)的簇状分布多形性钙化钼靶图像为研究对象,探求在设计乳腺钼靶图像分类器过程中提取ROI的新方法。结果显示,设计出优化的分类器后,可高效地识别试验对象,其测试集上的分类准确率最高可达99.3%。因此,本研究可为医生的临床研判提供辅助信息,并为细分BI-RADS4、进一步精准诊断奠定技术基础。
Abstract:
Mammography has been the preferred method for early detection and diagnosis of breast cancer. Extraction of the region of interest (ROI) of mammary molybdenum target image and then pattern recognition with artificial intelligence algorithm have been proved to ameliorate significantly the efficiency of breast cancer screening. The experimental images of this study are from an open database-digital database for screening mammography (DDSM). Taking the Clustered Distribution Pleomorphic Calcification (BI-RADS4) mammography as the research object, we explored a new method of extracting ROI designed for mammographic classifier. The results showed that the classifier optimized with the new method could identify the test object in a more efficient way, and the classification accuracy could be as high as 99.3%. This method can provide additional and useful information for clinical diagnosis, lay a technical foundation for subdividing BI-RADS4 and furthering accurate diagnosis.

参考文献/References

备注/Memo

备注/Memo:
(收稿日期:2019-09-19)国家重点研发计划项目(2016YFC1303003)。△通信作者Email: ychen@sandau.edu.cn
更新日期/Last Update: 2020-04-14