Brain Tumor Classification in MRI Scans Using Sparse Representation

  • Muhammad Nasir
  • Asim Baig
  • Aasia Khanum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8509)


Recent advancement in biomedical image processing using Magnetic Resonance Imaging (MRI) makes it possible to detect and localize brain tumors with ease. However, reliable classification of brain tumor types using MRI still remains a challenging problem. In this paper we propose a sparse representation based approach to successfully classify tumors in brain MRI. We aim to classify brain scans into eight (8) different categories with seven (7) indicating different tumor types and one for normal brain. This allows the proposed approach to not only classify brain tumors but also to detect their existence. The proposed classification approach is validated using Leave 2-Out cross-validation technique. The result obtained from the proposed approach is then compared with a recent technique presented in literature. The comparison clearly shows that the proposed approach outperforms the existing technique both in terms of accuracy and number of classes being employed.


Sparse Representation MRI Multi-class Classification Brain Tumor Medical Imaging 


  1. 1.
    Armstrong, T.S., Cohen, M.Z., Eriksen, L.R., Hickey, J.V.: Symptom clusters in oncology patients and implications for symptom research in people with primary brain tumors. Journal of Nursing Scholarship 36(3), 197–206 (2004)CrossRefGoogle Scholar
  2. 2.
    Faehndrich, J., Zanella, F.E., Hattingen, E.: Preoperative diagnostic imaging of brain tumors. Radiološki Arhiv Srbije (RAS) 16, 5–17 (2010)Google Scholar
  3. 3.
    Selvaraj, D., Dhanasekaran, R.: Segmentation of Cerebrospinal Fluid and Internal Brain Nuclei in Brain Magnetic Resonance Images. International Review on Computers & Software 8(5), 1063–1071 (2013)Google Scholar
  4. 4.
    Gordillo, N., Montseny, E., Sobrevilla, P.: State of the art survey on MRI brain tumor segmentation. Magnetic Resonance Imaging 31(8), 1426–1438 (2013)CrossRefGoogle Scholar
  5. 5.
    Kharrat, A., Gasmi, K., Messaoud, M.B.: A Hybrid Approach for Automatic Classification of Brain MRI Using Genetic Algorithm and Support Vector Machine. Leonardo Journal of Sciences, Issue 17, 71–82 (2010)Google Scholar
  6. 6.
    Zulpe, N., Pawar, V.: GLCM Textural Features for Brain Tumor Classification. International Journal of Computer Science Issues (IJCSI) 9(3), 354–359 (2012)Google Scholar
  7. 7.
    Javed, U., Riaz, M.M., Ghafoor, A.: MRI brain classification using texture features, fuzzy weighting and support vector machine. Progress In Electromagnetics Research B 53, 73–88 (2013)CrossRefGoogle Scholar
  8. 8.
    Patil, S., Udupi, V.R.: A computer aided diagnostic system for classification of brain tumors using texture features and probabilistic neural network. International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR) 3, 61–66 (2013)Google Scholar
  9. 9.
    Zacharaki, E.I., Kanas, V.G., Davatzikos, C.: Investigating machine learning techniques for MRI-based classification of brain neoplasms. International Journal of Computer Assisted Radiology and Surgery 6(6), 821–828 (2011)CrossRefGoogle Scholar
  10. 10.
    Ambrosini, R.D., Wang, P., ODell, W.G.: Computer aided detection of metastatic brain tumors using automated three dimensional template matching. Journal of Magnetic Resonance Imaging 31(1), 85–93 (2010)CrossRefGoogle Scholar
  11. 11.
    Arif, T., Shaaban, Z., Krekor, L., Baba, S.: Object Classification via Geometrical, Zernike And Legendre Moments. Journal of Theoretical & Applied Information Technology 6(3) (2009)Google Scholar
  12. 12.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust Face Recognition via Sparse Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2), 210–227 (2009)CrossRefGoogle Scholar
  13. 13.
    Zhang, D., Yang, M., Feng, X.: Sparse Representation or Collaborative Representation: Which Helps Face Recognition? In: 13th IEEE International Conference on Computer Vision (ICCV), pp. 471–478 (2011)Google Scholar
  14. 14.
    Harvard Medical School,

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Muhammad Nasir
    • 1
  • Asim Baig
    • 2
  • Aasia Khanum
    • 1
  1. 1.Department of Computer Engineering, College of E&MENational University of Sciences and Technology (NUST)IslamabadPakistan
  2. 2.SciFacterzIslamabadPakistan

Personalised recommendations