Abstract
Automatic segmentation methods for tumors are typically only suitable for a specific type of tumor in a specific imaging modality and sometimes lack in accuracy whereas manual tumor segmentation achieves the desired results but is very time consuming. Interactive segmentation however speeds up the process while still being able to maintain the accuracy of manual segmentation.
This paper presents a novel method for fast interactive segmentation of tumors (called FIST) from medical images, which is suitable for all somewhat spherical tumors in any 3d medical imaging modality. The user clicks in the center of the tumor and a belief propagation based iterative adaption process is initiated, thereby considering image gradients as well as local smoothness priors of the surface. During that process, instant visual feedback is given, enabling to intervene in the adaption process by sketching parts of the contour in any cross section.
The approach has successfully been applied to the segmentation of liver tumors in CT datasets. Satisfactory results could be achieved in 15.20875 seconds on the average. Further trials on oropharynx tumors, liver tumors and the prostate from MR images as well as lymph nodes and the bladder from CT volumes demonstrate the generality of the presented approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adams, R., Bischof, L.: Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(6), 641–647 (1994)
Barbu, A., Suehling, M., Xu, X., Liu, D., Zhou, S., Comaniciu, D.: Automatic Detection and Segmentation of Axillary Lymph Nodes. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 28–36. Springer, Heidelberg (2010)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001)
Boykov, Y.Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images. In: Proc. Eighth IEEE Int. Conf. Computer Vision, ICCV 2001, vol. 1, pp. 105–112 (2001)
Dornheim, L., Dornheim, J., Rossling, I., Monch, T.: Model-based segmentation of pathological lymph nodes in ct data, vol. 7623, p. 76234V. SPIE (2010)
Egger, J., Bauer, M., Kuhnt, D., Carl, B., Kappus, C., Freisleben, B., Nimsky, C.: Nugget-Cut: A Segmentation Scheme for Spherically- and Elliptically-Shaped 3d Objects. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds.) DAGM 2010. LNCS, vol. 6376, pp. 373–382. Springer, Heidelberg (2010)
Friedland, G., Jantz, K., Rojas, R.: Siox: simple interactive object extraction in still images. In: Proc. Seventh IEEE Int. Multimedia Symp. (2005)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1988)
McGuinness, K., O’Connor, N.E.: A comparative evaluation of interactive segmentation algorithms. Pattern Recognition 43(2), 434–444 (2010)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco (1988)
Salembier, P., Garrido, L.: Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval 9(4), 561–576 (2000)
Steger, S., Erdt, M.: Lymph node segmentation in ct images using a size invariant mass spring model. In: 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB) pp. 1–4 (2010)
Tappen, M.F., Freeman, W.T.: Comparison of graph cuts with belief propagation for stereo, using identical mrf parameters. In: Proc. Ninth IEEE Int. Computer Vision Conf., pp. 900–906 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Steger, S., Sakas, G. (2012). FIST: Fast Interactive Segmentation of Tumors. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2011. Lecture Notes in Computer Science, vol 7029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28557-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-28557-8_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28556-1
Online ISBN: 978-3-642-28557-8
eBook Packages: Computer ScienceComputer Science (R0)