Abstract
This paper presents a shape-based curve-growing algorithm for object recognition in the field of medical imaging. The proposed curve growing process, modeled by a Bayesian network, is influenced by both image data and prior knowledge of the shape of the curve. A maximum a posteriori (MAP) solution is derived using an energy-minimizing mechanism. It is implemented in an adaptive regularization framework that balances the influence of image data and shape prior in estimating the curve, and reflects the causal dependencies in the Bayesian network. The method effectively alleviates over-smoothing, an effect that can occur with other regularization methods. Moreover, the proposed framework also addresses initialization and local minima problems. Robustness and performance of the proposed method are demonstrated by segmentation of pulmonary fissures in computed tomography (CT) images.
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
Akgul, Y.S., Kambhamettu, C., Stone, M.: Automatic extraction and tracking of the tongue contours. IEEE Trans. Med. Imag. 18, 1035–1045 (1999)
Berger, M.O., Mohr, R.: Towards autonomy in active contour models. In: Tenth International Conference on Pattern Recognition, Atlantic City, U.S., pp. 847–851 (1990)
Betke, M., Wang, J.B., Ko, J.P.: An integrated chest CT image analysis system – BU-MIA. Tech. Report, Boston University, Computer Science Department (2003)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models - their training and application. Comput. Vis. Image Underst. 61, 38–59 (1995)
Giger, M.L., Vyborny, C.J.: Computers aid diagnosis of breast abnormalities. Diagn Imaging 15, 98–102 (1993)
Glazer, H.S., Anderson, D.J., DiCroce, J.J., et al.: Anatomy of the major fissure: evaluation with standard and thin-section CT. Radiology 180, 839–844 (1991)
Golland, P., Kikinis, R., Halle, M., et al.: AnatomyBrowser: A Novel Approach to Visualization and Integration of Medical Information. J. Computer Assisted Surg. 4, 129–143 (1999)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Comput. Vis. 1, 321–331 (1987)
Kubo, M., Kawata, Y., Niki, N., et al.: Automatic extraction of pulmonary fissures from multidetector-row CT images. In: Proceedings of the IEEE International Conference on Image Processing, Thessaloniki, Greece, pp. 1091–1094 (2001)
Li, S.Z.: Markov Random Field Modeling in Computer Vision. Springer, Heidelberg (1995)
Leventon, M.E., Grimson, W.E.L., Faugeras, O.: Statistical shape influence in geodesic active contours. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head, U.S. vol. I, pp. 316–323 (2000)
Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: A level set approach. IEEE Trans. Pattern Anal. Mach. Intell. 17, 158–175 (1995)
Pearl, J.: Probabilistic reasoning in intelligent systems: Networks of plausible inference, San Mateo, California. Morgan Kaufmann, San Francisco (1988)
Pham, D.L., Xu, C., Prince, J.L.: Current Methods in Medical Image Segmentation. Annual Review of Biomedical Engineering 2, 315+ (2000)
Wang, J., Betke, M., Ko, J.P.: Segmentation of pulmonary fissures on diagnostic CT – preliminary experience. In: Proceedings of the International Conference on Diagnostic Imaging and Analysis, Shanghai, China, pp. 107–112 (2002)
Williams, D.J., Shah, M.: A fast algorithm for active contours and curvature estimation. CVGIP: Image Understanding 55, 14–26 (1992)
Zhang, L.: Atlas-driven lung lobe segmentation in volumetric X-ray CT images. Ph.D. Thesis, University of Iowa (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, J., Betke, M., Ko, J.P. (2004). Shape-Based Curve Growing Model and Adaptive Regularization for Pulmonary Fissure Segmentation in CT. In: Barillot, C., Haynor, D.R., Hellier, P. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. MICCAI 2004. Lecture Notes in Computer Science, vol 3216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30135-6_66
Download citation
DOI: https://doi.org/10.1007/978-3-540-30135-6_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22976-6
Online ISBN: 978-3-540-30135-6
eBook Packages: Springer Book Archive