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Automatic Lung Nodule Detection Using Template Matching

  • Serhat Ozekes
  • A. Yilmaz Camurcu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4243)

Abstract

We have developed a computer-aided detection system for detecting lung nodules, which generally appear as circular areas of high opacity on serial-section CT images. Our method detected the regions of interest (ROIs) using the density values of pixels in CT images and scanning the pixels in 8 directions by using various thresholds. Then to reduce the number of ROIs the amounts of change in their locations based on the upper and the lower slices were examined, and finally a nodule template based algorithm was employed to categorize the ROIs according to their morphologies. To test the system’s efficiency, we applied it to 276 normal and abnormal CT images of 12 patients with 153 nodules. The experimental results showed that using three templates with diameters 8, 14 and 20 pixels, the system achieved 91%, 94% and 95% sensitivities with 0.7, 0.98 and 1.17 false positives per image respectively.

Keywords

Lung Nodule Template Match Black Pixel White Pixel Lung Nodule Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Greenlee, R.T., Nurray, T., Bolden, S., Wingo, P.A.: Cancer statistics 2000. CA Cancer J. Clin. 50, 7–33 (2000)CrossRefGoogle Scholar
  2. 2.
    Health and Welfare Statistics Association, J. Health Welfare Stat., 46, 50–51 (1999)Google Scholar
  3. 3.
    Ozekes, S., Osman, O., Camurcu, A.Y.: Mammographic Mass Detection Using A Mass Template. Korean J. Radiol. 6, 221–228 (2005)Google Scholar
  4. 4.
    Betke, M., Hong, H., Thomas, D., Prince, C., Ko, J.P.: Landmark detection in the chest and registration of lung surfaces with an application to nodule registration. Med. Image Anal. 7, 265–281 (2003)CrossRefGoogle Scholar
  5. 5.
    Lee, Y., Hara, T., Fujita, H., Itoh, S., Ishigaki, T.: Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Trans. Med. Imaging 20, 595–604 (2001)CrossRefGoogle Scholar
  6. 6.
    Farag, A.A., El-Baz, A., Gimel’farb, G., Falk, R.: Detection and recognition of lung abnormalities using deformable templates. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 3, pp. 738–741 (2004)Google Scholar
  7. 7.
    Samuel, G., Armato, III., Geoffrey, M., Michael, F., Charles, R., David, Y., et al.: Lung image database consortium -Developing a resource for the medical imaging research community. Radiology, 739–748 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Serhat Ozekes
    • 1
  • A. Yilmaz Camurcu
    • 2
  1. 1.Istanbul Commerce UniversityIstanbulTurkey
  2. 2.Marmara University, GoztepeIstanbulTURKEY

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