A Clinical Support System for Brain Tumor Classification Using Soft Computing Techniques

  • P. Rupa Ezhil ArasiEmail author
  • M. Suganthi
Image & Signal Processing
Part of the following topical collections:
  1. Wearable Computing Techniques for Smart Health


A brain tumor is an accumulation of abnormal cells in human brain. As tumor increases in size, it induces brain damage. Hence it is essential to diagnose the type of brain tumor. The effective modality used for brain tumor diagnose is MRI because of its remarkable image resolution, the speed of acquisition, and high safety profile for patients. The analysis of brain MRI is an important part of patient care and decision. Hence in the proposed Clinical Support System, the brain MRI image is preprocessed using Genetic Optimized Median Filter followed by brain tumor region segmentation using Hierarchical Fuzzy Clustering Algorithm. The features of the tumor region are extracted through GLCM feature extraction method. Lion Optimized Boosting Support Vector machine model is used for further classification of tumor by Brain Tumor Image Segmentation (BraTS) dataset. Hence the proposed clinical support system provides an integrated model for Detection and classification of brain tumor which assists the doctors in appropriate evaluation of tumor.


Brain tumor Preprocessing Segmentation Genetic optimized median filter Hierarchical fuzzy clustering GLCM Lion optimization technique Boosting support vector machine 


Compliance with ethical standards

Conflict of interest

We(Authors and Co-Authors) have no conflicts of Interests. The Paper is not submitted to any other Journals.

Ethical approval (Involving human participants and/or animals)

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

Informed consent

The article does not use any animal or human participants. So, it is not applicable.


  1. 1.
    Beddad, B., Hachemi, K. (2016) Brain tumor detection by using a modified FCM and Level set algorithms. Proceedings of fourth International Conference on Control Engineering & Information Technology (CEIT). pp:1–5.Google Scholar
  2. 2.
    Bhima, K., Jagan, A. (2016) Analysis of MRI based brain tumor identification using segmentation technique. Proceedings of International Conference on Communication and Signal Processing (ICCSP). pp: 2109–2113.Google Scholar
  3. 3.
    Caudra, M. B., Gomez, J., Hagmann, P., Pollo, C., Villemure, J. G., Dawant, B. M. and Thiran, J. Ph. (2002) Atlas-based segmentation of pathological brains using a model of tumor growth. Medical Image Computing and Computer Assisted Intervention MICCAI, pp. 380–387.Google Scholar
  4. 4.
    clark, M. C., Hall, L. O., goldgof, D. B., Velthuizen, R., Murtagh, F. R., and Silbiger, M. S., Automatic tumor segmentation using knowledge-based techniques. IEEE Transactions on Medical Imaging 17(2):187–201, 1998.CrossRefGoogle Scholar
  5. 5.
    Clark, M. C., Hall, L. O., Goldgof, D. B., Clarke, L. P., Velthuizen, R. P., and Silbiger, M. S., MRI segmentation using fuzzy clustering techniques. IEEE Engineering in Medicine and Biology 17(2):187–201, 1998.Google Scholar
  6. 6.
    El-Dihshan, E. A., Hosney, T., and Salem, A. B. M., Hybrid intelligence techniques for MRI brain image classification. Elsevier, Digital Signal Processing 20:433–441, 2010.CrossRefGoogle Scholar
  7. 7.
    Francis, K. J, Premi, M. S. G. (2015) Kernel weighted FCM based MR image segmentation for brain tumor detection. Proceedings of International Conference on Circuit, Power and Computing Technologies (ICCPCT). pp:1–6.Google Scholar
  8. 8.
    Gajanayake, G. M. N. R., Yapa, R. D. and Hewawithana, B. (2009) Comparison of Standard Image Segmentation Methods for Segmentation of Brain Tumors from 2D MR Images.4th International Conference on Industrial and Information Systems, ICIIS, University of Peradeniya, Sri Lanka, pp. 301–305.Google Scholar
  9. 9.
    Gonzalez, R. A. and Woods, R.E. (2002) Digital Image Processing. Second Edition. Prentice Hall.Google Scholar
  10. 10.
    Haritha, D. (2016) Comparative study on brain tumor detection techniques. Proceedings of International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES). pp: 1387–1392.Google Scholar
  11. 11.
    Isselmou, Abd El K, Zhang, S., Xu, G. (2016) A novel approach for brain tumor detection using MRI Images. Journal of Biomedical Science and Engineering. pp: 44–52.CrossRefGoogle Scholar
  12. 12.
    Jolliffe, I. T. (2002) Principal Component Analysis. SpringerVerlag, (New York).Google Scholar
  13. 13.
    Koli, M. A., (2012) Review of Impulse noise reduction techniques. International Journal on Computer Science and Engineering, Vol. 04, No. 2.Google Scholar
  14. 14.
    Kumar, N. and Nachamai, M. (2017) Noise Removal and Filtering Techniques Used in Medical Images. Oriental Journal of Computer Science and Technology. pp: 103–113.CrossRefGoogle Scholar
  15. 15.
    Lefohn, A., Cates, J. and Whitaker, R. (2003) Interactive, GPU- Based Level Sets for 3D BrainTumor Segmentation MICCAI, 2003.Google Scholar
  16. 16.
    Liang-Juan L, Yu-Long L (2010) A Hierarchical Fuzzy Clustering Algorithm. Proceedings of the International Conference on Computer Application and System Modeling 978–1–4244-7237-6.Google Scholar
  17. 17.
    Madheswaran, M., and Dhas, D. A. S., Classification of brain MRI images using support vector machine with various kernels. Biomedical Research 26(3):505–513, 2015.Google Scholar
  18. 18.
    Manoj, K. and Sourabh, Y. (2012) Brain tumor detection and segmentation using histogram thresholding .International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249–8958, Volume-1, Issue-4.Google Scholar
  19. 19.
    Megersa, Y., Alemu, G. (2015) Brain tumor detection and segmentation using hybrid intelligent algorithms. AFRICON. pp:1–8.Google Scholar
  20. 20.
    Michael, R. K., Simon, K. W., Arya, N., Peter, M. B., Ferenc, A. J., and Ron, K., Automated segmentation of MR images of brain tumors. Radiology 218:586–591, 2001.CrossRefGoogle Scholar
  21. 21.
    Nandi, A (2015) Detection of human brain tumour using MRI image segmentation and morphological operators. Proceedings of IEEE International Conference on Computer Graphics, Vision and Information Security. pp: 55–60.Google Scholar
  22. 22.
    Natarajan, P., Krishnan, N., Natasha, S., Shraiya, N., Bhuvanesh, P. (2012) Tumor Detection using threshold operation in MRI Brain Images. Proceedings of International Conference on Computational Intelligence and Computing Research, IEEE.Google Scholar
  23. 23.
    Nazir, M., Wahid, F., and Ali Khan, S., A simple and intelligent approach for brain MRI classification. Journal of Intelligent & Fuzzy Systems 28(3):1127–1135, 2015.Google Scholar
  24. 24.
    Saha, B. N., Ray, N., Greiner, R., Murtha, A., Zhang, H. (2012) Quick detection of brain tumors and edemas: A bounding box method using symmetry. Computerized Medical Imaging and Graphics 36 (2012) 95–107, Elsevier.Google Scholar
  25. 25.
    Singh, B., Aggarwal, P. (2017) Detection of brain tumor using modified mean-shift based fuzzy c-mean segmentation from MRI Images. Proceedings of 8 th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). pp:536–545.Google Scholar
  26. 26.
    Singh, G., Ansari, M. A. (2017) Efficient detection of brain tumor from MRIs using K-means segmentation and normalized histogram. Proceedings of first International Conference on Information Processing. pp: 1–6.Google Scholar
  27. 27.
    Sovilj-Nikic, S., Sovilj-Nikic, I. (2007) Application of Genetic Algorithm in Median Filtering. Proceedings of the International Multiconference on Computer Science and Information Technology, pp. 127–139.Google Scholar
  28. 28.
    Sumithra, M. G., Deepa, B. (2016) Performance analysis of various segmentation techniques for detection of brain abnormality. Proceedings of Region 10 IEEE Conference (TENCON).pp:2056–2061.Google Scholar
  29. 29.
    Suneetha, B., Jhansi Rani, A. (2017) A survey on image processing techniques for brain tumor detection using magnetic resonance imaging. Innovations in Green Energy and Healthcare Technologies (IGEHT) , pp.1–6.Google Scholar
  30. 30.
    Usman, M. A., Anam, U. (2011) Computer Aided system for Brain Tumor Detection and Segmentation. Computer Networks and information Technology (ICCNIT), IEEE.Google Scholar
  31. 31.
    Vaishali, S., Rao, K. K., Subba Rao, G. V. (2015) A review on noise reduction methods for brain MRI images. Proceedings of International Conference on Signal Processing and Communication Engineering Systems. pp: 363–365Google Scholar
  32. 32.
    Wang, B. X., and Japkowicz, N., Boosting support vector machines for imbalanced data sets. Knowledge and Information Systems 25(1):1–20, 2010.CrossRefGoogle Scholar
  33. 33.
    Wang, G., Li, W., Ourselin, S., Vercauteren, T.. (2018) Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Pages 179–190. Springer.Google Scholar
  34. 34.
    Wu, S., Hu, Y., Wang, W., Feng, X., and Shu, W. (2013) Application of Global Optimization Methods for Feature Selection and Machine Learning. Mathematical Problems in Engineering, Volume 2013, Article ID 241517, pp. 1–8.Google Scholar
  35. 35.
    Zabir, I., Paul, S., Rayhan, Md. Abu, Sarker, T., Fattah, S. A., Shahnaz, C. (2015) Automatic brain tumor detection and segmentation from multi-modal MRIimages based on region growing and level set evolution. Proceedings of International WIE Conference on Electrical and Computer Engineering (WIECON-ECE). pp: 503–506.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringMuthayammal Engineering CollegeTamilnaduIndia
  2. 2.Department of Electronics and Communication EngineeringMahendra College of EngineeringTamilnaduIndia

Personalised recommendations