Abstract
In this paper, an automatic knowledge-based framework for level set segmentation of 3D calvarial tumors from Computed Tomography images is presented. Calvarial tumors can be located in both soft and bone tissue, occupying wide range of image intensities, making automatic segmentation and computational modeling a challenging task. The objective of this study is to analyze and validate different approaches in intensity priors modeling with an attention to multiclass problems. One, two, and three class Gaussian mixture models and a discrete model are evaluated considering probability density modeling accuracy and segmentation outcome. Segmentation results were validated in comparison to manually segmented golden standards, using analysis in ROC (Receiver Operating Curve) space and Dice similarity coefficient.
Chapter PDF
Similar content being viewed by others
Keywords
- Gaussian Mixture Model
- Segmentation Accuracy
- Medical Image Computing
- Multiclass Problem
- Prior Probability Density Function
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
Popovic, A., Engelhardt, M., Wu, T., Portheine, F., Schmieder, K., Radermacher, K.: CRANIO - Computer Assisted Planning for Navigation and Robot-assisted Surgery on the Skull. In: Lemke, H., Vannier, M., Inamura, K., Farman, A., Doi, K., Reiber, J. (eds.) ICALP 1997. International Congress Series, vol. 1256, pp. 1269–1276. Elsevier, Amsterdam (2003)
Grunert, P., Espinosa, J., Darabi, K., Filippi, R.: Computer-aided Navigation in Neurosurgery. Neurosurg. Rev. 26, 73–99 (2003)
Grover, S., Aggarwal, A., Uppal, P.S., Tandon, R.: The CT Triad of Malignacy in Meningioma - Redefinition, with a Report of Three New Cases. Neuroradiology 45, 799–803 (2003)
Popovic, A., Engelhardt, M., Radermacher, K.: Segmentation of Skull-infltrated Tumors Using ITK: Methods and Validation. In: ISC/NA-MIC/MICCAI Workshop on Open-Source Software at MICCAI 2005 (2005)
Popovic, A., Engelhardt, M., Radermacher, K.: Knowledge-based segmentation of calvarial tumors in computed tomography images. In: Bildverarbeitung für Medizin, BVM 2006. Informatik-Aktuell, pp. 151–155. Springer, Heidelberg (2006)
Ruf, A., Greenspan, H., Goldberger, J.: Tissue classiffcation of noisy mr brain images using constrained gmm. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 790–797. Springer, Heidelberg (2005)
Touhami, W., Boukerroui, D., Cocquerez, J.P.: Fully automatic kidneys detection in 2d ct images: A statistical approach. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 262–269. Springer, Heidelberg (2005)
Ho, S., Bullitt, E., Gerig, G.: Level Set Evolution with Region Competition: Automatic 3-D Segmentation of Brain Tumors. In: Proceedings of 16th International Conference on Pattern Recognition (ICPR), vol. 1, pp. 532–535 (2002)
Colliot, O., Mansi, T., Bernasconi, N., Naessens, V., Klironomos, D., Bernasconi, A.: Segmentation of focal cortical dysplasia lesions using a feature-based level set. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 375–382. Springer, Heidelberg (2005)
Leventon, M., Grimson, E., Faugeras, O., Kikinis, R., Wells III, W.: Knowledge-based Segmentation of Medical Images. In: Geometric Level Set Methods in Imaging, Vision, and Graphics, pp. 401–420. Springer, Heidelberg (2003)
Marin, J.M., Mengersen, K., Robert, C.P.: Bayesian modelling and inference on mixtures of distributions, ch. 16. In: Handbook of Statistics, vol. 25, Elsevier, Amsterdam (2005)
Statistical Validation of Image Segmntation Quality Based on a Spatial Overlap Index. Acad. Radiol. 11, 178–189 (2004)
Zijdenbos, A., Dawant, B., Margolin, R., Palmer, A.: Morphometric Analysis of White Matter Lesions in MR Images: Method and Validation. IEEE Transactions on Medical Imaging 13, 716–724 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Popovic, A., Wu, T., Engelhardt, M., Radermacher, K. (2006). Modeling of Intensity Priors for Knowledge-Based Level Set Algorithm in Calvarial Tumors Segmentation. In: Larsen, R., Nielsen, M., Sporring, J. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. MICCAI 2006. Lecture Notes in Computer Science, vol 4191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11866763_106
Download citation
DOI: https://doi.org/10.1007/11866763_106
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44727-6
Online ISBN: 978-3-540-44728-3
eBook Packages: Computer ScienceComputer Science (R0)