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Classification of Brain Tumor MRIs Using a Kernel Support Vector Machine

  • Mahmoud Khaled Abd-Ellah
  • Ali Ismail AwadEmail author
  • Ashraf A. M. Khalaf
  • Hesham F. A. Hamed
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 636)

Abstract

The use of medical images has been continuously increasing, which makes manual investigations of every image a difficult task. This study focuses on classifying brain magnetic resonance images (MRIs) as normal, where a brain tumor is absent, or as abnormal, where a brain tumor is present. A hybrid intelligent system for automatic brain tumor detection and MRI classification is proposed. This system assists radiologists in interpreting the MRIs, improves the brain tumor diagnostic accuracy, and directs the focus toward the abnormal images only. The proposed computer-aided diagnosis (CAD) system consists of five steps: MRI preprocessing to remove the background noise, image segmentation by combining Otsu binarization and K-means clustering, feature extraction using the discrete wavelet transform (DWT) approach, and dimensionality reduction of the features by applying the principal component analysis (PCA) method. The major features were submitted to a kernel support vector machine (KSVM) for performing the MRI classification. The performance evaluation of the proposed system measured a maximum classification accuracy of 100 % using an available MRIs database. The processing time for all processes was recorded as 1.23 seconds. The obtained results have demonstrated the superiority of the proposed system.

Keywords

Brain tumor MRIs classification K-means DWT PCA KSVM 

References

  1. 1.
    Logeswari, T., Karnan, M.: An improved implementation of brain tumor detection using segmentation based on hierarchical self organizing map. Int. J. Comput. Theor. Eng. 2(4), 591 (2010)CrossRefGoogle Scholar
  2. 2.
    El-Dahshan, E.S.A., Mohsen, H.M., Revett, K., Salem, A.B.M.: Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst. Appl. 41(11), 5526–5545 (2014)CrossRefGoogle Scholar
  3. 3.
    Jayadevappa, D., Srinivas Kumar, S., Murty, D.: Medical image segmentation algorithms using deformable models: a review. IETE Tech. Rev. 28(3), 248–255 (2011)CrossRefGoogle Scholar
  4. 4.
    Yazdani, S., Yusof, R., Karimian, A., Pashna, M., Hematian, A.: Image segmentation methods and applications in MRI brain images. IETE Tech. Rev. 32(6), 413–427 (2015)CrossRefGoogle Scholar
  5. 5.
    Abedini, M., Codella, N.C.F., Connell, J.H., Garnavi, R., Merler, M., Pankanti, S., Smith, J.R., Syeda-Mahmood, T.: A generalized framework for medical image classification and recognition. IBM J. Res. Dev. 59(2/3), 1–18 (2015)CrossRefGoogle Scholar
  6. 6.
    Prastawa, M., Bullitt, E., Moon, N., Van Leemput, K., Gerig, G.: Automatic brain tumor segmentation by subject specific modification of atlas priors. Acad. Radiol. 10(12), 1341–1348 (2003)CrossRefGoogle Scholar
  7. 7.
    Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Med. Image Anal. 8(3), 275–283 (2004)CrossRefGoogle Scholar
  8. 8.
    Saha, B.N., Ray, N., Greiner, R., Murtha, A., Zhang, H.: Quick detection of brain tumors and edemas: a bounding box method using symmetry. Comput. Med. Imaging Graph. 36(2), 95–107 (2012)CrossRefGoogle Scholar
  9. 9.
    Gordillo, N., Montseny, E., Sobrevilla, P.: State of the art survey on MRI brain tumor segmentation. Magn. Reson. Imaging 31(8), 1426–1438 (2013)CrossRefGoogle Scholar
  10. 10.
    Nabizadeh, N., Kubat, M.: Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features. Comput. Electr. Eng. 45, 286–301 (2015)CrossRefGoogle Scholar
  11. 11.
    Zhang, N., Ruan, S., Lebonvallet, S., Liao, Q., Zhu, Y.: Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation. Comput. Vis. Image Underst. 115(2), 256–269 (2011)CrossRefGoogle Scholar
  12. 12.
    Aslam, A., Khan, E., Beg, M.S.: Improved edge detection algorithm for brain tumor segmentation. Procedia Comput. Sci. 58, 430–437 (2015). Second International Symposium on Computer Vision and the Internet (VisionNet 15)CrossRefGoogle Scholar
  13. 13.
    Abdel-Maksoud, E., Elmogy, M., Al-Awadi, R.: Brain tumor segmentation based on a hybrid clustering technique. Egypt. Inf. J. 16(1), 71–81 (2015)CrossRefGoogle Scholar
  14. 14.
    Ayachi, R., Ben Amor, N.: Brain tumor segmentation using support vector machines. In: Sossai, C., Chemello, G. (eds.) ECSQARU 2009. LNCS, vol. 5590, pp. 736–747. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Bauer, S., Nolte, L.-P., Reyes, M.: Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 354–361. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Natteshan, N.V.S., Angel Arul Jothi, J.: Automatic classification of brain MRI images using SVM and neural network classifiers. In: El-Alfy, E.-S., Thampi, S.M., Takagi, H., Piramuthu, S., Hanne, T. (eds.) Advances in Intelligent Informatics. AISC, vol. 320, pp. 19–30. Springer, Heidelberg (2015)Google Scholar
  17. 17.
    Toennies, K.D.: Guide to Medical Image Analysis: Methods and Algorithms. Advances in Computer Vision and Pattern Recognition. Springer Science & Business Media, Heidelberg (2012)CrossRefGoogle Scholar
  18. 18.
    Youlian Zhu, C.H.: An improved median filtering algorithm for image noise reduction. In: 2012 International Conference on Solid State Devices and Materials Science, pp. 609–616. Elsevier (2012)Google Scholar
  19. 19.
    Somasundaram, K., Genish, T.: Modified Otsu thresholding technique. In: Balasubramaniam, P., Uthayakumar, R. (eds.) ICMMSC 2012. CCIS, vol. 283, pp. 445–448. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  20. 20.
    Cheng, J., Xiaoyun Chen, H.: An enhanced k-means algorithm using agglomerative hierarchical clustering strategy. In: International Conference on Automatic Control and Artificial Intelligence (ACAI 2012), 3–5 March, pp. 407–410. IEEE (2012)Google Scholar
  21. 21.
    Abo-Zahhad, M., Gharieb, R.R., Ahmed, S.M., Abd-Ellah, M.K.: Huffman image compression incorporating DPCM and DWT. J. Signal Inf. Process. 6, 123–135 (2015)Google Scholar
  22. 22.
    Zhang, Y., Wu, L., Wei, G.: A new classifier for polarimetric SAR images. Prog. Electromagnet. Res. 94, 83–104 (2009)CrossRefGoogle Scholar
  23. 23.
    Kolusheva, S., Yossef, R., Kugel, A., Hanin-Avraham, N., Cohen, M., Rubin, E., Porgador, A.: A novel “reactomics” approach for cancer diagnostics. Sensors 12(5), 5572–5585 (2012)CrossRefGoogle Scholar
  24. 24.
    Wang, H., Fei, B.: A modified fuzzy c-means classification method using a multiscale diffusion filtering scheme. Med. Image Anal. 13(2), 193–202 (2009). Includes Special Section on Functional Imaging and Modelling of the HeartMathSciNetCrossRefGoogle Scholar
  25. 25.
    Arimura, H., Tokunaga, C., Yamashita, Y., Kuwazuru, J.: Magnetic resonance image analysis for brain CAD systems with machine learning. In: Suzuki, K. (ed.) Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, pp. 258–296. IGI Gloabal, Hershey (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mahmoud Khaled Abd-Ellah
    • 1
  • Ali Ismail Awad
    • 2
    • 3
    Email author
  • Ashraf A. M. Khalaf
    • 4
  • Hesham F. A. Hamed
    • 4
  1. 1.Electronic and Communication DepartmentAl-Madina Higher Institute for Engineering and TechnologyGizaEgypt
  2. 2.Department of Computer Science, Electrical and Space EngineeringLuleå University of TechnologyLuleåSweden
  3. 3.Faculty of EngineeringAl Azhar UniversityQenaEgypt
  4. 4.Electrical Engineering Department, Faculty of EngineeringMinia UniversityMiniaEgypt

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