期刊文献+

基于乳腺肿瘤超声图像的灰色理论识别

The Recognition of Grey Theory of Breast Tumor Based on Ultrasonic Image
下载PDF
导出
摘要 乳腺肿瘤边界特征量如似圆度,面积比率,边界粗糙度、长宽比等,是鉴别良、恶性肿瘤的重要特征。利用灰色理论中的待诊病例与良性、恶性肿瘤的关联度的阀值,即可判断出是良性还是恶性肿瘤。实验表明,在76幅待诊病例中,良性肿瘤识别正确率达到87.5%,恶性肿瘤识别正确率达到75%。该方法较为有效地识别良性、恶性乳腺肿瘤。 Parameters for margin features of breast tumor such as circularity,area ratio,contour roughness,length width ratio are the important characteristics to identify the benign or malignant tumor.The purpose of this article is to perform the diagnosis based on grey relation between the cases that to be diagnosed and benign or malignant tumor.Results from 76 ultrasonic images have shown that the identification accuracy of benign or malignant tumor are 87.5% and75% respectively.The methods applied in this paper can make an effective diagnosis for breast tumor.
出处 《山西电子技术》 2010年第5期9-10,16,共3页 Shanxi Electronic Technology
关键词 乳腺肿瘤 超声图像 边界特征 灰色关联 breast tumor ultrasonic image contour feature grey relation
  • 相关文献

参考文献8

  • 1American Cancer Society.Cancer facts and figures,2001.
  • 2王世萍,周慧.彩色多普勒超声在鉴别乳腺肿瘤良恶性中的价值[J].交通医学,2009,23(1):103-105. 被引量:3
  • 3Stavros A,Thickman D,Rapp C,et al.Solid Breast Nodules:Use of Sonography to Distinguish Between Benign and Malignant Lesions[J].Radiology,1995,196:123.
  • 4Yi Zheng,James F,John J Gisvold.Reduction of Breast Biopsies with a Modified Self-organizing Map[J].IEEE Transactions on Neural Networks,1997,8(6):2.
  • 5Giger M L,AI-Hallaq H,Huo Z,et al.Computerized Analysis of Lesions in US Images of the Breast[J].Acad Radiol,1999,6:665.
  • 6Chandra M,Theodore W,Sarah A.Computer-based Margin Analysis of Breast Sonography for Diffentiating Malignant and Benign Masses[J].J Ultrasound Med,2004,23:1201.
  • 7张科宏,彭玉兰,李德玉,林江莉,罗燕,汪天富,蒋银宝.基于边界特征的乳腺肿瘤超声图像识别[J].生物医学工程学杂志,2006,23(6):1237-1240. 被引量:5
  • 8邓聚龙.灰色控制系统[M].武汉:华中理工大学出版社,1990..

二级参考文献14

  • 1成映富.彩色多普勒超声联合近红外光扫描对乳腺癌的诊断价值[J].交通医学,2006,20(5):603-603. 被引量:5
  • 2American Cancer Society.Cancer facts and figures; 2001
  • 3Stavros A,Thickman D,Rapp C,et al.Solid breast nodules:use of sonography to distinguish between benign and malignant lesions.Radiology,1995;196:123
  • 4Yi Zheng,James F.John J Gisvold.Reduction of breast biopsies with a modified self-organizing Map.IEEE Transactions on Neural Networks,1997; 8 (6):2
  • 5Giger ML,Al-Hallaq H,Huo Z,et al.Computerized analysis of lesions in US images of the breast.Acad Radiol,1999;6:665
  • 6Sahiner B,LeCarpentier GL,Chan HP,et al.Computerized characterization of breast masses using three-dimensional ultrasound images.Proc SPIE Med Imaging,1998;3338:301
  • 7Chandra M,Theodore W,Sarah A.Computer-based margin analysis of breast sonography for diffentiating malignant and benign masses.J Ultrasound Med,2004; 23:1201
  • 8Kilday J,Palmieri F.Fox MD.Classsifying mamographic lesions using computerized image analysis.IEEE Trans Med Imaging,1993;12:664
  • 9YI-Hong Chou,Chui-Mei Tiu,et al.Stepwise logistic regression analysis of tumor contour features for breast ultrasound diagnosis.Ultrasound in Med & Bio,2001;27(11):1493
  • 10Chen DR,Chang RF,Huang YL.Computer-aided diagnosis applied to US of solid breast nodules by using nueral networks.Radiology,1999;213:407

共引文献13

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部