Abstract
A new method that automatically detects and segments brain tumors in 3D MR images is presented. An initial detection is performed by a fuzzy possibilistic clustering technique and morphological operations, while a deformable model is used to achieve a precise segmentation. This method has been successfully applied on five 3D images with tumors of different sizes and different locations, showing that the combination of region-based and contour-based methods improves the segmentation of brain tumors.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Capelle, A.S., Colot, O., Fernandez-Maloigne, C.: Evidential segmentation scheme of multi-echo MR images for the detection of brain tumors using neighborhood information. Information Fusion 5, 203–216 (2004)
Clark, M.C., Lawrence, L.O., Golgof, D.B., Velthuizen, R., Murtagh, F.R., Silbiger, M.S.: Automatic tumor segmentation using knowledge-based techniques. IEEE Transaction on Medical Imaging 17(2) (April 1998)
Colliot, O., Camara, O., Dewynter, R., Bloch, I.: Description of brain internal structures by means of spatial relations for MR image segmentation. In: The International Society of Optical Engineering. SPIE 2004 Medical Imaging, vol. 5370, pp. 444–455 (2004)
Cuadra, M.B., Pollo, C., Bardera, A., Cuisenaire, O., Villemure, J., Thiran, J.: Atlas-based segmentation of pathological MR brain images using a model of lesion growth. IEEE Transactions on Medical Imaging 23(10), 1301–1313 (2004)
Ho, S., Bullitt, E., Gerig, G.: Level set evolution with region competition: Automatic 3D segmentation of brain tumors. In: International Conference on Pattern Recognition, pp. 532–535 (2002)
Kaus, M.R., Warfield, S.K., Nabavi, A., Chatzidakis, E., Black, P.M., Jolesz, F.A., Kikinis, R.: Segmentation of meningiomas and low grade gliomas in MRI. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 1–10. Springer, Heidelberg (1999)
Lefohn, A., Cates, J., Whitaker, R.: Interactive, GPU-based level sets for 3d brain tumor segmentation. Technical report, University of Utah (April 2003)
Mangin, J.-F., Coulon, O., Frouin, V.: Robust brain segmentation using histogram scale-space analysis and mathematical morphology. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 1230–1241. Springer, Heidelberg (1998)
Moon, N., Bullitt, E., Leemput, K.V., Gerig, G.: Model-based brain and tumor segmentation. In: Intenational Conference on Pattern Recognition, vol. 1, pp. 526–531 (2002)
Moonis, G., Liu, J., Udupa, J.K., Hackney, D.B.: Estimation of tumor volume with fuzzy-connectedness segmentation of MR images. American Journal of Neuroradiology 23, 352–363 (2002)
Pal, N.R., Pal, K., Bezdek, J.C.: C Bezdek. A mixed c-mean clustering model. In: IEEE International Conference on Fuzzy Systems, pp. 11–21 (1997)
Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Medical Image Analysis 18(3), 217–231 (2004)
Soltanian-Zadeh, H., Kharrat, M., Donald, P.J.: Polynomial transformation for MRI feature extraction. In: SPIE, vol. 4322, pp. 1151–1161 (2001)
Xu, C., Prince, J.L.: Snakes, shapes and gradient vector flow. IEEE transaction on Image Processing 7, 359–369 (1998)
Zhu, Y., Yang, H.: Computerized tumor boundary detection using a Hopfield neural network. IEEE Transactions on Medical Imaging 16(1), 55–67 (1997)
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
Khotanlou, H., Atif, J., Colliot, O., Bloch, I. (2006). 3D Brain Tumor Segmentation Using Fuzzy Classification and Deformable Models. In: Bloch, I., Petrosino, A., Tettamanzi, A.G.B. (eds) Fuzzy Logic and Applications. WILF 2005. Lecture Notes in Computer Science(), vol 3849. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11676935_39
Download citation
DOI: https://doi.org/10.1007/11676935_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32529-1
Online ISBN: 978-3-540-32530-7
eBook Packages: Computer ScienceComputer Science (R0)