Advertisement

Automated Brain Tumor Diagnosis and Severity Analysis from Brain MRI

  • Sabyasachi Mukherjee
  • Oishila BandyopadhyayEmail author
  • Arindam Biswas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10149)

Abstract

Analysis of brain MRI is of utmost importance as it reveals the underlying details of the main controlling portion of human body. In this paper, we have proposed a fully automated approach to differentiate abnormal brain images from healthy MRI. The proposed method segments out the tumor region from the abnormal MRI by analyzing the energy profile of the image pixels. After tumor segmentation, the tumor features are analyzed to classify the degree of malignancy. This approach can be applied to segment both high grade and low grade tumors.

Keywords

Brain MRI Tumor Edema Dead cell Image energy 

Notes

Acknowledgement

Authors would like to acknowledge Department of Science & Technology, Government of India, for financial support vide ref. no. SR/WOS-A/ET-1022/2014 under Woman Scientist Scheme to carry out this work.

References

  1. 1.
    Akkus, Z., Sedlar, J., Coufalova, L., Korfiatis, P., Kline, T.L., Warner, J.D., Agrawal, J., Erickson, B.J.: Semi-automated segmentation of pre-operative low grade gliomas in magnetic resonance imaging. Cancer Imaging 15(12), 1–11 (2015)Google Scholar
  2. 2.
    Ashburner, J., Friston, K.J.: Unified segmentation. NeuroImage 26, 839–851 (2005)CrossRefGoogle Scholar
  3. 3.
    Bauer, S., Fejes, T., Slotboom, J., Wiest, R., Nolte, L.P., Reyes, M.: Segmentation of brain tumor images based on integrated hierarchical classification and regularization. In: Proceedings of Multimodal Brain Tumor Segmentation Challenge (MICCAI-BRATS), pp. 10–13 (2012)Google Scholar
  4. 4.
    Bauer, S., Wiest, R., Nolte, L.P., Reyes, M.: A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58(13), 97–129 (2013)CrossRefGoogle Scholar
  5. 5.
    Charutha, S., Jayashree, M.J.: An efficient brain tumor detection by integrating modified texture based region growing and cellular automata edge detection. In: Proceedings of IEEE Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. 1193–1199 (2014)Google Scholar
  6. 6.
    Dvorak, P., Kropatsch, W., Walter, K.: Automatic detection of brain tumors in MR images. In: Proceedings of IEEE Telecommunications and Signal Processing (TSP), pp. 577–580 (2013)Google Scholar
  7. 7.
    Eis, M., Els, T., Hoehn-Berlage, M., Hossmann, K.A.: Quantitative diffusion MR imaging in cerebral tumor and edema. In: Ito, U., Baethmann, A., Hossmann, K.-A., Kuroiwa, T., Marmarou, A., Reulen, H.-J., Takakura, K. (eds.) Brain Edema IX, pp. 344–346. Springer, Vienna (1994)CrossRefGoogle Scholar
  8. 8.
    Khotanlou, H., Colliot, O., Bloch, I.: Automatic brain tumor segmentation using symmetry analysis and deformable models. In: Proceedings of Advances in Pattern Recognition (ICAPR), pp. 198–202 (2007)Google Scholar
  9. 9.
    Louis, D.N., Ohgaki, H., Wiestler, O.D., Cavenee, W.K., Burger, P.C., Jouvet, A., Scheithauer, B.W., Kleihues, P.: The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 114, 97–109 (2007)Google Scholar
  10. 10.
    Maiti, I., Chakraborty, M.: A new method for brain tumor segmentation based on watershed and edge detection algorithms in HSV colour model. In: Proceedings of Computing and Communication Systems (NCCCS), pp. 1–5 (2012)Google Scholar
  11. 11.
    Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRefGoogle Scholar
  12. 12.
    Menze, B.H., et al.: A generative probabilistic model and discriminative extensions for brain lesion segmentation with application to tumor and stroke. IEEE Trans. Med. Imaging 35(4), 933–946 (2016)CrossRefGoogle Scholar
  13. 13.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979)CrossRefGoogle Scholar
  14. 14.
    Pedoia, V., Binaghi, E., Balbi, S., Benedictis, A.D., Monti, E., Minotto, R.: Glial brain tumor detection by using symmetry analysis. In: Proceedings of SPIE Medical Imaging, p. 831445 (2012)Google Scholar
  15. 15.
    Selvaraj, H., Selvi, S.T., Selvathi, D., Gewali, L.: Brain MRI slices classification using least squares support vector machine. Int. J. Intell. Comput. Med. Sci. Image Process. 1(1), 21–33 (2007)Google Scholar
  16. 16.
    Somasundaram, K., Kalaiselvi, T.: Automatic detection of brain tumor from MRI scans using maxima transform. In: Proceedings of National Conference on Image Processing (NCIMP), pp. 136–141 (2010)Google Scholar
  17. 17.
    Umbaugh, S.E.: Feature Analysis and Pattern Classification. CRC Press, Boca Raton (2011)Google Scholar
  18. 18.
    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, 256–269 (2011)CrossRefGoogle Scholar
  19. 19.
    Zikic, D., Glocker, B., Konukoglu, E., Shotton, J., Criminisi, A., Ye, D.H., Demiralp, C., Thomas, O.M., Das, T., Jena, R., Price, S.J.: Context-sensitive classification forests for segmentation of brain tumor tissues. In: Proceedings of Multimodal Brain Tumor Segmentation Challenge (MICCAI-BRATS), pp. 1–9 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sabyasachi Mukherjee
    • 1
  • Oishila Bandyopadhyay
    • 2
    Email author
  • Arindam Biswas
    • 1
  1. 1.Department of Information TechnologyIndian Institute of Engineering Science and TechnologyHowrahIndia
  2. 2.Advanced Computing and Microelectronics Unit, Indian Statistical InstituteKolkataIndia

Personalised recommendations