Abstract
This paper examines the problem of obtaining a representation of the three-dimensional(3-D) pulmonary nodule images, which is a key problem in discriminating benign and malignant nodules for differential diagnosis of the lung cancer using thin-section CT images. A curvature based approach is developed with the aim of characterizing internal intensity structures of benign and malignant nodules. This approach makes use of curvature indexes to represent locally each voxel in a three-dimensional (3-D) pulmonary nodule image. From the distribution of curvature indexes and CT value over the 3-D pulmonary nodule image a set of histogram features is computed for global characterization of benign and malignant nodules. Linear discriminant analysis is used for classification and leave-one-out method is used to evaluate the classification accuracy. Compared with the performance of experienced physicians the potential usefulness of the curvature based features in the computer-aided differential diagnosis is demonstrated by using receiver operating characteristic (ROC) curves as the performance measure.
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
Kaneko, M., Eguchi, K., Ohmatsu, H., Kakinuma, R., Naruke, T., Suemasu, K., Moriyama, N.: Peripheral lung cancer: Screening and detection with low-dose spiral CT versus radiography. Radiology 201, 798–802 (1996)
Mori, K., Saitou, Y., Tominaga, K., Yokoi, K., Miyazawa, N., Okuyama, A., Sasagawa, M.: Small nodular lesions in the lung periphery: New approach to diagnosis with CT. Radiology 177, 843–849 (1990)
Siegelman, S.S., Zerhouni, E.A., Leo, F.P., Khouri, N.F., Stitik, F.P.: CT of the solitary pulmonary nodule. AJR 135, 1–13 (1980)
Proto, A.V., Thomas, S.R.: Pulmonary nodules studied by computed tomography. Radiology 156, 149–153 (1985)
McNitt-Gray, M.F., Hart, E.M., Goldin, J., Yao, C.W., Aberle, D.R.: A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution computed tomography. In: Proc. SPIE, vol. 2710, pp. 1024–1034 (1996)
Tozaki, T., Kawata, Y., Niki, N., Ohmatsu, H., Eguchi, K., Moriyama, N.: Pulmonary organs analysis for differential diagnosis based on thoracic thin-section CT images. IEEE Trans. Nuclear Science 45, 3075–3082 (1998)
Hirano, Y., Mekada, Y., Hasegawa, J., Toriwaki, J., Ohmatsu, H., Eguchi, K.: Quantification of vessels convergence in three-dimensional chest X-ray CT images with three-dimensional concentration index. Medical Imaging Tchnology 15, 228–235 (1997)
Kawata, Y., Niki, N., Ohmatsu, H., Kakinuma, R., Eguchi, K., Kaneko, M., Moriyama, N.: Quantitative surface characterization pulmonary nodules based on thin-section CT images. IEEE Trans. Nuclear Science 45, 2132–2138 (1998)
Kitaoka, H., Takaki, R., Itho, K., Kobatake, H., Ohmatsu, H., Moriyama, N., Eguchi, K.: Shape analysis of pulmonary nodules in 3D-CT images with a new method of curvature estimation. In: Lemke, H.U., Vannier, M.W., Inamura, K. (eds.) Proc. Computer Assisted Radiology and Surgery (CAR 1998), pp. 51–56 (1998)
Bejer, J., Liebig, T., Bittner, R.C., Wust, P., Fleck, E., Felix, R.: Surface analysis of pulmonary lesions using fractal features. In: Lemke, H.U., Vannier, M.W., Inamura, K. (eds.) Proc. Computer Assisted Radiology and Surgery (CAR 1997), pp. 228–233 (1997)
Kawata, Y., Niki, N., Ohmatsu, H., Kakinuma, R., Mori, K., Eguchi, K., Kaneko, M., Moriyama, N.: Curvature base analysis of internal structure of pulmonary nodules using thin-section CT images. In: Proc. IEEE Int. Conf. Image Processing, vol. III, pp. 851–855 (1998)
Hasegawa, J., Mori, K., Toriwaki, J., Anno, H., Katada, K.: Automated extraction of lung cancer lesions from multi-slice chest CT images by using tree-dimensional image processing. IEICE Trans. J76-D-II, 1587–1594 (1993)
Caselles, V., Kimmel, R., Sapiro, G., Sbert, C.: Minimal surfaces: A three dimensional segmentation approach, Technion Technical Report, 973, Israel (1995)
Koenderink, J.J., Van Doorn, A.J.: Surface shape and curvature scales. Image and Vision Computing 10, 557–565 (1992)
Dorai, C., Jain, A.K.: COSMOS-A Representation scheme for 3D free-form objects. IEEE Trans. Pattern Anal. Machine Intell. 19, 1115–1130 (1997)
Thirion, J.-P., Gourdon, A.: Computing the differential characteristics of isointensity surfaces. Comput. Vision and Image Understanding 61, 190–202 (1995)
Aylward, S., Pizer, S., Bullitt, E., Eberly, D.: Intensity ridge and widths for tubular object segmentation and description. In: Proc. Mathematical Methods in Biomedical Image Analysis, pp. 131–138 (1996)
Sato, Y., Nakajima, S., Atsumi, H., Koller, T., Gerig, G., Yoshida, S., Kikinis, R.: Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Medical Image Analysis 2, 143–163 (1998)
Duda, R.O., Hart, P.E.: Pattern classification and scene analysis. John Wiley & Sons, Chichester (1973)
Fukunaga, K.: Introduction to statistical pattern recognition, 2nd edn. Academic Press, Inc, London (1990)
Costanza, M.C., Afifi, A.A.: Comparison of stopping rules in forward step wise discriminant analysis. J. of the American Statistical Association 74, 777–785 (1979)
Metz, C.E.: ROC methodology in radiologic imaging. Investigative Radiology 21, 720–733 (1986)
Lindeberg, T.: Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention. Int. J. Computer Vision 11, 283–318 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kawata, Y. et al. (1999). Potential Usefulness of Curvature Based Description for Differential Diagnosis of Pulmonary Nodules. In: Taylor, C., Colchester, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI’99. MICCAI 1999. Lecture Notes in Computer Science, vol 1679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10704282_42
Download citation
DOI: https://doi.org/10.1007/10704282_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66503-8
Online ISBN: 978-3-540-48232-1
eBook Packages: Springer Book Archive