Academic Radiology
Volume 14, Issue 5 , Pages 579-593 , May 2007

Automated Detection of Small Pulmonary Nodules in Whole Lung CT Scans

  • Andinet A. Enquobahrie, MS

      Affiliations

    • School of Electrical and Computer Engineering, Rhodes Hall, Cornell University, Ithaca, NY 14850
    • Corresponding Author InformationAddress correspondence to A.A.E.
    • ,
  • Anthony P. Reeves, PhD

      Affiliations

    • School of Electrical and Computer Engineering, Rhodes Hall, Cornell University, Ithaca, NY 14850
    • ,
  • David F. Yankelevitz, MD

      Affiliations

    • Department of Radiology, Room J-030, Weill Medical College of Cornell University, 525 East 68th Street, New York, NY 10021.
    • ,
  • Claudia I. Henschke, PhD, MD

      Affiliations

    • Department of Radiology, Room J-030, Weill Medical College of Cornell University, 525 East 68th Street, New York, NY 10021.

Received 3 December 2006 ,Accepted 1 January 2007.

References 

  1. American Cancer Society. Cancer Facts and Figures 2006. Atlanta: ACS; 2006;
  2. Henschke CI, McCauley DI, Yankelevitz DF, et al. Early Lung Cancer Action Project: Overall design and findings from baseline screening. Lancet. 1999;354:99–105
  3. Henschke CI, Yankelevitz DF, Smith JP, et al. CT screening for lung cancer: Assessing a regimen’s diagnostic performance. Clin Imaging. 2004;28:317–321
  4. Armato SG, Giger ML, Blackburn JT, et al. Three-dimensional approach to lung nodule detection in helical CT. Proc SPIE. 1999;3661:553–559
  5. Gurcan MK, Petrick N, Sahiner B, et al. Computerized lung nodule detection on thoracic CT images: Combined rule-based and statistical classifier for false positive reduction. Proc SPIE. 2001;4322:686–692
  6. Lee Y, Hara T, Fujita H, et al. Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Trans Med Imaging. 2001;20:595–604
  7. Lu X, Wei GQ, Qian J, Jain AK. Learning-based pulmonary nodule detection from multislice CT data. 2004;Computer Assisted Radiology and Surgery (CARS), Proceedings of the 18th International Congress.
  8. McCulloch CC, Kaucic RA, Mendonca PRS, Walter DJ, Avila RS. Model-based detection of lung nodules in computer tomography exams. Acad Radiol. 2004;11:258–266
  9. Arimura H, Katsuragawa S, Suzuki K, et al. Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. Acad Radiol. 2004;11:617–629
  10. Paik DS, Beaulieu CF, Rubin GD, et al. Surface normal overlap: A computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT. IEEE Trans Med Imaging. 2004;23:661–675
  11. Soler L, Delingette H, Malandain G, et al. Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery. Comput Aided Surg. 2001;6:131–142
  12. Kostis WJ, Reeves AP, Yankelevitz DF, Henschke CI. Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical (CT) Images. IEEE Trans Med Imaging. 2003;22:1259–1274
  13. Takizawa H, Yammamoto S, Matsumoto T, Tatento Y, Linuma T, Matsumoto M. Recognition of lung nodules from X-ray CT Images using 3-D Markov random field models. Proc SPIE. 2002;4684:716–725
  14. Fukano G, Takizawa H, Shigemoto K, et al. Recognition method of lung nodules using blood vessel extraction techniques and 3-D object models. Proc SPIE. 2003;5032:190–196
  15. Brown MS, McNitt-Gray MF, Goldin JG, Suh RD, Sayre JW, Aberle DR. Patient-specific models for lung nodule detection and surveillance in CT Images. IEEE Trans Med Imaging. 2001;20:1242–1250
  16. Henschke CI, Yankelevitz DF, Mirtcheva R, McGuninness G, McCauley D, Miettinen OS. CT Screening for lung cancer: Frequency and significance of part-solid and non-solid nodules. AJR Am J Roentgenol. 2002;178:1053–1057
  17. Engeler CE, Tashjian JH, Trenkner SW, Walsh JW. Ground-glass opacity of the lung parenchyma: A guide to analysis with high-resolution CT. AJR Am J Roentgenol. 1993;160:249–251
  18. Yoon HE, Fukuhara K, Michiura T, Takada M, Imakita M, Nonaka K. Pulmonary nodules 10 mm or less in diameter with ground-glass opacity component detected by high resolution computed tomography hava a high possibility of malignancy. Jpn Thorac Cardiovasc Surg. 2005;53:22–28

PII: S1076-6332(07)00071-2

doi: 10.1016/j.acra.2007.01.029

Academic Radiology
Volume 14, Issue 5 , Pages 579-593 , May 2007

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