期刊文献+

基于CT影像分析的肺结节计算机辅助检测与诊断技术进展 被引量:1

Technological progress of computer-aided detection and diagnosis of lung nodule based on CT image analysis
原文传递
导出
摘要 肺结节是肺部最常见的病变之一,肺结节的早期检测和诊断对于肺癌的早期诊治十分重要.近年来,随着多层螺旋CT(MSCT)、高分辨CT(HRCT)及低剂量胸部CT(LDCT)的应用,计算机辅助诊断(CAD)系统的必要性和重要性也日益显现.由于CAD系统可以明显提高诊断医生的工作效率,为更多的患者服务,因此成为国内外相关领域专家的研究热点,近几年来也取得了一定的成果.就肺结节的CT计算机辅助检测和诊断的基本方法和应用作一综述. Lung nodules are one of the most common pathological changes, thus early detection of lung nodule is very important for the diagnosis medical treatment of lung eancer. In recent years, as the application of multi-slice spiral CT(MSCT), high-resolution CT(HRCT) and low-dose chest CTCLDCT), computer-aided diagnosis (CAD) system will be more essential and more important. Since CAD system can improve the working efficiency of doctors and provide service to more patients, has become the research hotspot and achievement has been made in relevant area internationally recently. This review summarizes the basic methods and applieations of computer-aided detection and diagnosis of lung nodule based on CT image.
作者 李丽 邱天爽
出处 《国际生物医学工程杂志》 CAS 北大核心 2009年第5期283-286,共5页 International Journal of Biomedical Engineering
基金 国家自然科学基金(30570475,60872122)
关键词 肺结节 计算机辅助诊断 假阳性 Lung nodule Computer-aided detection False positive
  • 相关文献

同被引文献20

  • 1Withey DJ, Koles ZJ. Medical image segmentation: methods and software: 2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source hnaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, Hangzhou, October 12-14, 2007[C]. New York: IEEE Xplore Digital library, 2007: 140-143.
  • 2Jayadevappa D, Kumar SS, Murty DS. Medical image segmentation algorithms using deformable models: a review [J]. IETE Tech Rev, 2011, 28(3): 248-255.
  • 3Sharma N, Aggarwal LM. Automated medical image segmentation techniques[J]. J Med Phys, 2010, 35(1): 3-14.
  • 4Fenster A, Chiu B. Evaluation of Segmentation algorithms for Medi- cal hnaging[J]. Conf Proc IEEE Eng Med Biol Soc, 2005, 7: 7186- 7189.
  • 5Grosgeorge D, Petitjean C, Caudron J, et al. Automatic cardiac ven- tricle segmentation in MR images: a validation study[J]. Int J Comput Assist Radiol Surg, 2011, 6(5): 573-581.
  • 6Salman Y, Assal M, Badawi A, et al. Validation techniques for quan- titative brain tumors measurements[J]. Conf Proc IEEE Eng Med Biol Soc, 2005, 7:7048-7051.
  • 7Warfield SK, Zou KH, Wells WM. Validation of image segmentation by estimating rater bias and variance[J]. Phil Trans R Soc A, 2008, 366(1874): 2361-2375.
  • 8Jannin P, Fitzpatrick JM, Hawkes DJ, et al. Validation of medical image processing in image-guided therapy[J]. IEEE Trans Med Imag- ing, 2002, 21(12): 1445-1449.
  • 9Hamarueh G, Jassi P, Tang Li-sa. Simulation of ground-truth valida- tion data via physically- and statistically-based warps[J]. Med Image Comput Comput Assist Interv, 2008, 11(1): 459-467.
  • 10Kauppinen H, Sepanen T. An experiment comparison of autoregres-sive and Fourier-hased descriptors in 2D shape classification [J1- IEEE Trans Pattern Anal Mach Intell, 1995, 17(2): 201-207.

引证文献1

二级引证文献2

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