Abstract
In this paper, we present a novel automated method for detecting tumor location in brain magnetic resonance (MR) images, and identifying the tumor boundary. This method employs an unsupervised learning algorithm called Force for coarse detection of the tumor region. Once tumor area is identified, further processing is done in the local neighborhood of the tumor to determine its boundary. The Force method, which is based on the rules of electrostatics, is used for finding spatial clusters of high intensity in the 2D space of MR image. Further analysis of the identified clusters is performed to select the cluster that contains the tumor. This method outperforms many existing methods due to its accuracy and speed. The performance of the proposed method has been verified by examining MR images of different patients.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bezdek, J.C., Hall, L.O., Clarke, L.P.: Review of MR image segmentation techniques using pattern recognition. Medical physics 20(4), 1033–1048 (1993)
Held, K., Rota, K.E., Krause, B.J., Wells III, W.M., Kikinis, R., Muller-Gartner, H.W.: Markov random field segmentation of brain MR images. IEEE Trans. Med. Imaging, 878–886 (1997)
Clarke, L., et al.: MRI segmentation: Methods and applications. Magnetic Resonance Imaging 13(3), 343–368 (1995)
Cuadra, M.B., Gomez, J., Hagmann, P., Pollo, C., Villemure, J.G., Dawant, B.M., Thiran, J.: Atlas-based segmentation of pathological brains using a model of tumor growth. In: Medical Image Comp.& Computer-Assisted Intervention MICCAI (2002), pp. 380–387. Springer, Heidelberg (2002)
Clark, M.C., Hall, L.O., Goldgof, D.B., Velthuizen, R., Murtagh, F.R., Silbiger, M.S.: Automatic tumor-segmentation using knowledge-based techniques. IEEE Trans. on Medical Imaging 17, 187–201 (1998)
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)
Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A Brain Tumor Segmentation Framework Based on Outlier Detection. Medical Image Analysis 8, 275–283 (2004)
Gering, D., Eric, W., Grimson, L., Kikinis, R.: Recognizing Deviations from Normalcy for Brain Tumor Segmentation. Medical Image Computing and Computer-Assisted Intervention, 388–395 (September 2002)
Phooi Yee Lau Voon, F.C.T., Ozawa, S.: The detection and visualization of brain tumors on T2-weighted MRI images using multi parameter feature blocks. In: Engineering in Medicine and Biology Society. IEEE-EMBS, pp. 5104–5107 (2005)
Wu, M.-N., Lin, C.-C., Chang, C.-C.: Brain Tumor Detection Using Color-Based K-Means Clustering Seg. In: IEEE Computer Society, IIH-MSP 2007, pp. 245–250 (2007)
Kalantari Khandani, M., Saeedi, P., Fallah, Y.P., Khandani, M.K.: A Novel Data Clustering Algorithm Based on Electrostatic Field Concepts. In: IEEE Symposium on Computational Intelligence and Data Mining (IEEE CIDM 2009), pp. 232–237 (2009)
Mc Queen, J.: Some methods for classification and analysis of multivariate observations. In: Proc. of the 5th Berkeley Symp. on Mathematical Statistics and Probability, pp. 281–297 (1967)
Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence, 679–698 (1986)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Khandani, M.K., Bajcsy, R., Fallah, Y.P. (2009). Automated Segmentation of Brain Tumors in MRI Using Force Data Clustering Algorithm. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-10331-5_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10330-8
Online ISBN: 978-3-642-10331-5
eBook Packages: Computer ScienceComputer Science (R0)