Abstract
Two novel methods are proposed for robust segmentation of pulmonary nodules in CT images. The proposed solutions locate and segment a nodule in a semi-automatic fashion with a marker indicating the target. The solutions are motivated for handling the difficulty to segment juxtapleural, or wall-attached, nodules by using only local information without a global lung segmentation. They are realized as extensions of the recently proposed robust Gaussian fitting approach. Algorithms based on i) 3D morphological opening with anisotropic structuring element and ii) extended mean shift with a Gaussian repelling prior are presented. They are empirically compared against the robust Gaussian fitting solution by using a large clinical high-resolution CT dataset. The results show 8% increase, resulting in 95% correct segmentation rate for the dataset.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Reeves, A.P., Kostis, W.J.: Computer-aided diagnosis of small pulmonary nodules. Seminars in Ultrasound, CT, and MRI 21, 116–128 (2000)
van Ginneken, B., ter Harr Romeny, B.M., Viergever, M.A.: Computer-aided diagnosis in chest radiography: A survey. IEEE Trans. Med. Imag. 20, 1228–1241 (2001)
Ko, J.P., Naidich, D.P.: Computer-aided diagnosis and the evaluation of lung disease. J. Thorac Imag. 19, 136–155 (2004)
Ko, J.P., et al.: Small pulmonary nodules: Volume measurement at chest CT - phantom study. Radiology 228, 864–870 (2003)
Wormanns, D., et al.: Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reproducibility. Eur. Radiol. 14, 86–92 (2004)
Zhao, B., et al.: Two-dimensional multi-criterion segmentation of pulmonary nodules on helical CT images. IEEE Trans. Med. Imag. 22, 1259–1274 (2003)
Kostis, W.J., Reeves, A.P., Yankelevitz, D.F., Henschke, C.I.: Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Trans. Med. Imag. 22, 1259–1274 (2003)
Okada, K., Comaniciu, D., Krishnan, A.: Robust anisotropic Gaussian fitting for volumetric characterization of pulmonary nodules in multislice CT. IEEE Trans. Med. Imag. 24, 409–423 (2005)
Armato, S., et al.: Computerized detection of pulmonary nodules on CT scans. RadioGraphics 19, 1303–1311 (1999)
Wang, P., O’Dell, W.: Automated detection of juxta-pleural lung nodules in chest CT using lung contour corrected by anatomic landmarks. In: AAPM (2004)
Shen, H., Liang, L., Shao, M., Qing, S.: Tracing based segmentation for the labeling of individual rib structures in chest CT volume data. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 967–974. Springer, Heidelberg (2004)
Okada, K., Comaniciu, D., Krishnan, A.: Scale selection for anisotropic scale-space: Application to volumetric tumor characterization. In: IEEE Conf. Comput. Vision and Pat. Recog. (2004)
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. Imag. 20, 595–604 (2001)
Novak, C., Shen, H., Odry, B., Ko, J., Naidich, D.: System for automatic detection of lung nodules exhibiting growth. SPIE Med. Imag. (2004)
Comaniciu, D.: An algorithm for data-driven bandwidth selection. IEEE Trans. Pat. Anal. Mach. Intell. 25, 281–288 (2003)
Henschke, C.I., et al.: CT screening for lung cancer: frequency and significance of part-solid and non-solid nodules. AJR Am. J. Roentgenol. 178, 1053–1057 (2002)
Okada, K., Comaniciu, D., Krishnan, A.: Robust 3D segmentation of pulmonary nodules in multislice CT images. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 881–889. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Okada, K., Ramesh, V., Krishnan, A., Singh, M., Akdemir, U. (2005). Robust Pulmonary Nodule Segmentation in CT: Improving Performance for Juxtapleural Cases. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3750. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566489_96
Download citation
DOI: https://doi.org/10.1007/11566489_96
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29326-2
Online ISBN: 978-3-540-32095-1
eBook Packages: Computer ScienceComputer Science (R0)