Abstract
Producing consistent segmentations of lung nodules in CT scans is a persistent problem of image processing algorithms. Many hard-segmentation approaches are proposed in the literature, but soft segmentation of lung nodules remains largely unexplored. In this paper, we propose a classification-based approach based on pixel-level texture features that produces soft (probabilistic) segmentations. We tested this classifier on the publicly available Lung Image Database Consortium (LIDC) dataset. We further refined the classification results with a post-processing algorithm based on the variability index. The algorithm performed well on nodules not adjacent to the chest wall, producing a soft overlap between radiologists’ based segmentation and computer-based segmentation of 0.52. In the long term, these soft segmentations will be useful for representing the uncertainty in nodule boundaries that is manifest in radiological image segmentations.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ko, J.P., Betke, M.: Chest CT: automated nodule detection and assessment of change over time–preliminary experience. Radiology 218, 267–273 (2001)
Armato, S.G., McLennan, G., McNitt-Gray, M.F., Meyer, C.R., Yankelevitz, D., Aberle, D.R., Henschke, C.I., Hoffman, E.A., Kazerooni, E.A., MacMahon, H., Reeves, A.P., Croft, B.Y., Clarke, L.P.: Lung image database consortium: developing a resource for the medical imaging research community. Radiology 232, 739–748 (2004)
Armato, S.G., Roberts, R.Y., Kocherginsky, M., Aberle, D.R., Kazerooni, E.A., Macmahon, H., van Beek, E.J., Yankelevitz, D., McLennan, G., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Caligiuri, P., Quint, L.E., Sundaram, B., Croft, B.Y., Clarke, L.P.: Assessment of radiologist performance in the detection of lung nodules: dependence on the definition of “truth”. Acad. Radiol. 16, 28–38 (2009)
Siena, S., Zinoveva, O., Raicu, D., Furst, J., Armato III, S.: A shape-dependent variability metric for evaluating panel segmentations with a case study on LIDC. In: Karssemeijer, N., Summers, R.M. (eds.), 1st edn., pp. 762416–762418. SPIE, San Diego (2010)
Liu, S., Li, J.: Automatic medical image segmentation using gradient and intensity combined level set method. In: Conf. Proc. IEEE Eng. Med. Biol. Soc., vol. 1, pp. 3118–3121 (2006)
Dehmeshki, J., Amin, H., Valdivieso, M., Ye, X.: Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach. IEEE Trans. Med. Imaging 27, 467–480 (2008)
Xu, N., Ahuja, N., Bansal, R.: Automated lung nodule segmentation using dynamic programming and EM-based classification. In: Sonka, M., Fitzpatrick, J.M. (eds.), 1st edn., pp. 666–676. SPIE, San Diego (2002)
Wang, Q., Song, E., Jin, R., Han, P., Wang, X., Zhou, Y., Zeng, J.: Segmentation of lung nodules in computed tomography images using dynamic programming and multidirection fusion techniques. Acad. Radiol. 16, 678–688 (2009)
Wang, J., Engelmann, R., Li, Q.: Computer-aided diagnosis: a 3D segmentation method for lung nodules in CT images by use of a spiral-scanning technique. In: Giger, M.L., Karssemeijer, N. (eds.), 1st edn., pp. 69151–69158. SPIE, San Diego (2008)
Tang, H., Dillenseger, J.L., Bao, X.D., Luo, L.M.: A vectorial image soft segmentation method based on neighborhood weighted Gaussian mixture model. Comput. Med. Imaging Graph 33, 644–650 (2009)
Hongmin, C., Verma, R., Yangming, O., Seung-koo, L., Melhem, E.R., Davatzikos, C.: Probabilistic segmentation of brain tumors based on multi-modality magnetic resonance images. In: 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2007, pp. 600–603 (2007)
van Ginneken, B.: Supervised probabilistic segmentation of pulmonary nodules in CT scans. Med. Image Comput. Comput. Assist. Interv. 9, 912–919 (2006)
Lam, M.O., Disney, T., Raicu, D.S., Furst, J., Channin, D.S.: BRISC-an open source pulmonary nodule image retrieval framework. J. Digit Imaging 20(suppl. 1), 63–71 (2007)
Savitzky, A., Golay, M.J.E.: Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry 36, 1627–1639 (1964)
Kubota, T., Jerebko, A.K., Dewan, M., Salganicoff, M., Krishnan, A.: Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models. Med. Image Analysis 15, 133–154 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zinoveva, O., Zinovev, D., Siena, S.A., Raicu, D.S., Furst, J., Armato, S.G. (2011). A Texture-Based Probabilistic Approach for Lung Nodule Segmentation. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21596-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-21596-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21595-7
Online ISBN: 978-3-642-21596-4
eBook Packages: Computer ScienceComputer Science (R0)