Abstract
This paper presents a new general automatic method for segmenting brain tumors in magnetic resonance (MR) images. Our approach addresses all types of brain tumors. The proposed method involves, subsequently, image pre-processing, feature extraction via wavelet transform (WT), dimensionality reduction using genetic algorithm (GA) and classification of the extracted features using support vector machine (SVM). For the segmentation of brain tumor these optimal features are employed. The resulting method is aimed at early tumor diagnostics support by distinguishing between the brain tissue, benign tumor and malignant tumor tissue. The segmentation results on different types of brain tissue are evaluated by comparison with manual segmentation as well as with other existing techniques.
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.
References
AntolÃk, J., Hsu, W.H.: Evolutionary tree genetic programming. In: GECCO, pp. 1789–1790 (2005)
Bai, L., Liu, Y., Auer, D., Morgan, P.: Automatic segmentation of putamen from brain mri. In: IPCV, pp. 489–495 (2006)
Benamrane, N., Freville, A., Nekkache, R.: A hybrid fuzzy neural networks for the detection of tumors in medical images. American Journal of Applied Sciences 2(4), 892–896 (2005)
Chaplot, S., Patnaik, L.M., Jagannathan, N.R.: Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomedical Signal Processing and Control 1(1), 86–92 (2006)
Clark, M.C., Hall, L.O., Goldgof, D.B., Velthuizen, R., Reed, F., Silbiger, M.S.: Automatic tumor segmentation using knowledge-based techniques. IEEE Transactions on Medical Imaging 17, 187–201 (1998)
Clausi, D.A.: An analysis of co-occurrence texture statistics as a function of grey level quantization. Design 28(1), 45–62 (2002)
Ferri, F.J., Pudil, P., Hatef, M., Kittler, J.: Comparative study of techniques for large-scale feature selection (1994)
Fodor, I.: A survey of dimension reduction techniques. Technical report (2002)
Giger, M.L., Chan, H., Boone, J.: Anniversary paper: History and status of cad and quantitative image analysis: the role of medical physics and aapm. Med. Phys. 35(12), 5799–5820 (2008)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)
Gonzales, R.C., Wintz, P.: Digital image processing, 2nd edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1987)
Haralick, R., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Transactions on Systems, Man, and Cybernetics 3(6) (1973)
Ho, S., Bullitt, E., Gerig, G.: Level-set evolution with region competition: Automatic 3-d segmentation of brain tumors. In: ICPR, vol. 1, pp. 532–535 (2002)
Kharrat, A., Benamrane, N., Messaoud, M.B., Abid, M.: Evolutionary support vector machine for parameters optimization applied to medical diagnostic. In: VISAPP, pp. 201–204 (2011)
Kharrat, A., Gasmi, K., Messaoud, M.B., Benamrane, N., Abid, M.: Automated classification of magnetic resonance brain images using wavelet genetic algorithm and support vector machine. In: IEEE ICCI, pp. 369–374 (2010)
Khotanlou, H., Colliot, O., Atif, J., Bloch, I.: 3d brain tumor segmentation in mri using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Sets and Systems 160(10), 1457–1473 (2009)
Lecoeur, J., Barillot, C.: Segmentation d’images cérébrales: État de l’art. Research Report RR-6306, INRIA (2007)
Materka, A., Strzelecki, M.: Texture Analysis, Methods A Review, Technical report, Institute of Electronics, Technical University of Lodz (1998)
Balchandran, R., Mammone, R.: Non-parametric estimation and correction of non-linear distortion in speech system, pp. 749–752 (May 1998)
Raymer, M.L., Punch III, W.F., Goodman, E.D., Kuhn, L.A., Jain, A.K.: Dimensionality reduction using genetic algorithms. IEEE Trans. Evolutionary Computation 4(2), 164–171 (2000)
Russ, J.C.: The image processing handbook, 2nd edn. CRC Press, Inc., Boca Raton (1995)
Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
Harvard Medical Schooll, http://med.harvard.edu/AANLIB/
Soh, L.K., Tsatsoulis, C.: Texture analysis of sar sea ice imagery using gray level co-occurrence matrices. IEEE Transactions on Geoscience and Remote Sensing 37(2), 780–795 (1999)
Vapnik, V.N.: Estimation of dependences based on empirical data (1979) (in Russian)
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
Gasmi, K., Kharrat, A., Messaoud, M.B., Abid, M. (2012). Automated Segmentation of Brain Tumor Using Optimal Texture Features and Support Vector Machine Classifier. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31298-4_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-31298-4_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31297-7
Online ISBN: 978-3-642-31298-4
eBook Packages: Computer ScienceComputer Science (R0)