Journal of Medical Systems

, 42:251 | Cite as

Mixture Model Segmentation System for Parasagittal Meningioma brain Tumor Classification based on Hybrid Feature Vector

  • L. Arokia Jesu PrabhuEmail author
  • A. Jayachandran
Image & Signal Processing
Part of the following topical collections:
  1. Advancements in Internet of Medical Things for Healthcare System


Meningioma is the one of the most common type of brain tumor, it as arises from the meninges and encloses the spine and the brain inside the skull. It accounts for 30% of all types of brain tumor. Meningioma’s can occur in many parts of the brain and accordingly it is named. In this paper, a mixture model based classification of meningioma brain tumor using MRI image is developed. The proposed method consists of four stages. In the first stage, with respect to the cells’ boundary, it is necessary to further processing, which ensures the boundary of some cells is a discrete region. Mathematical Morphology brings a fancy result during the discrete processing. Accurate cancer cell nucleus segmentation is necessary for automated cytological image analysis. Thresholding is a crucial step in segmentation..An adaptive binarization technique is an important step for medical image analysis.Finally, a novel hybrid Fuzzy SVM is designed in the classification stage meningioma brain tumor. The tumor classification results of proposed feature extraction with SVM is 74.24%, MM with FSVM is 82.67% and MM with RBF is 62.71% and our proposed method MM with Hybrid SVM is 91.64%.


MRI Texture Feature extraction Classification brain tumor SVM 


Compliance with ethical standards

Conflict of Interest

No potential conflict of interest was reported by the authors.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Ramsay, C. R., Matowe, L., Grilli, R., Grimshaw, J. M., and Thomas, R. E., Interrupted time series designs in health technology assessment: lessons from two systematic reviews of behavior change strategies. Int. J. Technol. Assess. Health Care 19(4):613–623, 2003.CrossRefGoogle Scholar
  2. 2.
    Zikic, B., Glocker, E. K., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O. M., Das, T., Jena, R., and Price, S. J., Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel. Journal of Medical Image Computing and Computer Assisted Intervention 7512:369–376, 2012.Google Scholar
  3. 3.
    Dhanasekaran, R., and Jayachandran, A., Severity analysis of brain tumor in MRI images uses modified multi-texton structure descriptor and kernel-SVM. Arab. J. Sci. Eng. 39(10):7073–7086, 2014.CrossRefGoogle Scholar
  4. 4.
    Dubey, M. H., Gupta, S. K., and Gupta, S. K., Semi-automatic Segmentation of MRI Brain Tumor. Journal of Graphics, Vision and Image Processing. 9:33–40, 2009.Google Scholar
  5. 5.
    Kromer, C., Xu, J., Ostrom, Q. T. et al., Estimating the annual frequency of synchronous brain metastasis in the United States 2010-2013: a population-based study. J. Neuro-Oncol. 134(1):55–64, 2017.CrossRefGoogle Scholar
  6. 6.
    Posner, J. B., and Chernik, N. L., Intracranial metastases from systemic cancer. Adv. Neurol. 19:579–592, 1978.PubMedGoogle Scholar
  7. 7.
    Vishvaksenan, K. S., Mithra, K., Kalidoss, R., and Karthipan, R., Experimental study on Elliot wave theory for Handoff Prediction. Fluctuation and Noise Letters 15(4):1–11, 2016.CrossRefGoogle Scholar
  8. 8.
    Taheri, S., Ong, S. H., and Chong, V. F. H., Level-set segmentation of brain tumors using a threshold-based speed function. J. Image Vision Comput. 28:26–37, 2010.CrossRefGoogle Scholar
  9. 9.
    DeAngelis, L. M., and Posner, J. B., Neurologic Complications of Cancer. 2nd edition. New York: Oxford University Press, 2009.Google Scholar
  10. 10.
    Silberstein, S. D., Practice parameter: evidence-based guidelines for migraine headache (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 55(6):754–762, 2000.CrossRefGoogle Scholar
  11. 11.
    Krumholz, A., Wiebe, S., Gronseth, G. et al., Practice Parameter: evaluat- ing an apparent unprovoked first seizure in adults (an evidence-based review): report of the Quality Standards Subcommittee of the Ameri- can Academy of Neurology and the American Epilepsy Society. Neurology 69(21):1996–2007, 2007.CrossRefGoogle Scholar
  12. 12.
    Jayachandran, A., and Dhanasekaran, R., Brain tumor severity analysis using modified multi-texton histogram and hybrid kernel SVM. Int. J. Imaging Syst. Technol. 24(1):72–82, 2014.CrossRefGoogle Scholar
  13. 13.
    Glantz, M. J., Cole, B. F., Glantz, L. K. et al., Cerebrospinal fluid cytology in patients with cancer: minimizing false-negative results. Cancer 82(4):733–739, 1998.CrossRefGoogle Scholar
  14. 14.
    Del Principe, M. I., Buccisano, F., Cefalo, M. et al., High sensitivity of flow cytometry improves detection of occult leptomeningeal disease in acute lymphoblastic leukemia and lymphoblastic lymphoma. Ann. Hematol. 93(9):1509–1513, 2014.CrossRefGoogle Scholar
  15. 15.
    Abdel-Maksoud, E., Elmogy, M., and Al-Awadi, R., Brain tumor segmentation based on a hybrid clustering technique. Egypt. Informatics J. 16:71–81, 2015.CrossRefGoogle Scholar
  16. 16.
    Jayachandran, A., and Dhanasekaran, R., Automatic detection of brain tumor in magnetic resonance images using multi-texton histogram and support vector machine. Int. J. Imaging Syst. Technol. 23:97–103, 2013.CrossRefGoogle Scholar
  17. 17.
    Jayachandran, A., and Dhanasekaran, R., Abnormality segmentation and Classification of multi model brain tumor in MR images using Fuzzy based hybrid kernel SVM. International Journal of Fuzzy Systems 17(3):434–443, 2015.CrossRefGoogle Scholar
  18. 18.
    Patchell, R. A., Tibbs, P. A., Walsh, J. W. et al., A randomized trial of surgery in the treatment of single metastases to the brain. N. Engl. J. Med. 322(8):494–500, 1990.CrossRefGoogle Scholar
  19. 19.
    Hariharan, G., and Jayachandran, A., Color, textures and shape descriptor based cervical cancer classification system of pap smear images. J. Comput. Theor. Nanosci. 14(7):3609–3614, 2017.CrossRefGoogle Scholar
  20. 20.
    Vishvaksenan, K. S., Kalaiarasan, R., Kalidoss, R., and Karthipan, R., Real time experimental study and analysis of Elliott wave theory in signal strength prediction. Proceedings of National Academy of Sciences, Springer 88(1):107–119, 2018.Google Scholar
  21. 21.
    Luts, J., Laudadio, T., Idema, A. J., Simonetti, A. W., Heerschap, A., Vandermeulen, D., Suykens, J. A. K., and Huffel, S. V., Nosologic imaging: segmentation and classification using MRI and MRSI. Journal of NMR in Biomedicine 22:374–390, 2009.CrossRefGoogle Scholar
  22. 22.
    Borgelt, B., Gelber, R., Kramer, S. et al., The palliation of brain metasta- ses: final results of the first two studies by the Radiation Therapy Oncology Group. Int J RadiatOncolBiol Phys. 6(1):1–9, 1980.Google Scholar
  23. 23.
    Jayachandran, A., and Dhanasekaran, R., Multi Class Brain Tumor Classification Of MRI Images using Hybrid Structure Descriptor and Fuzzy Logic Based RBF Kernel SVM. Iranian Journal of Fuzzy system 14(3):41–54, 2017.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of CSEChandy College of EngineerinTuticorinIndia
  2. 2.Department of CSEPSN College of Engineering and TechnologyTirunelveliIndia

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