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Information Retrieves from Brain MRI Images for Tumor Detection Using Hybrid Technique K-means and Artificial Neural Network (KMANN)

  • Manorama SharmaEmail author
  • G. N. Purohit
  • Saurabh Mukherjee
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 4)

Abstract

Medical imaging plays a significant role in the field of medical science. In present scenario image segmentation is used to extract abnormal tissues from normal tissues clearly in medical images. Tumor detection through brain MRI using automatic system is effective and consumes lesser time which helps doctor in diagnosis. A Tumor can convert into cancer, which is major leading cause of death. Automation of tumor detection is required for detecting tumor on early stage. Proposed work presents hybrid technique for information retrieval from brain MRI images. This research work presents an efficient technique based on K-means and artificial neural network (KMANN). GLCM (Grey Level co-occurrence matrix) used for feature extraction. Fuzzy Inference System is created using extracted feature which followed by thresholding, morphological operator and Watershed segmentation for brain tumor detection. Proposed method is used to identifying affected part of brain and size of tumor from MRI image with the help of MATLAB R2013b is used.

Keywords

Watershed Threshold Morphological operator K-mean ANN 

References

  1. 1.
    Dong, B., Chien, A., & Shen, Z. (2010). Frame based segmentation for medical images. Communications in Mathematical Sciences9(2), 551–559.Google Scholar
  2. 2.
    Acharya, J., Gadhiya, S., & Raviya, K. (2013). Segmentation techniques for image analysis: A review. International Journal of computer science and management research2(1), 1218–1221.Google Scholar
  3. 3.
    Naik, D., & Shah, P. (2014). A review on image segmentation clustering algorithms. Int J Comput Sci Inform Technol5(3), 3289–93.Google Scholar
  4. 4.
    Christe, S. A., Malathy, K., & Kandaswamy, A. (2010). Improved hybrid segmentation of brain MRI tissue and tumor using statistical features. ICTACT J Image Video Process1(1), 34–49.Google Scholar
  5. 5.
    Seerha, G. K., & Kaur, R. (2013). Review on recent image segmentation techniques. International Journal on Computer Science and Engineering5(2), 109.Google Scholar
  6. 6.
    Goswami, S., & Bhaiya, L. K. P. (2013, October). A hybrid neuro-fuzzy approach for brain abnormality detection using GLCM based feature extraction. In Emerging Trends in Communication, Control, Signal Processing & Computing Applications (C2SPCA), 2013 International Conference on (pp. 1–7). IEEE.Google Scholar
  7. 7.
    Othman, M. F., & Basri, M. A. M. (2011, January). Probabilistic neural network for brain tumor classification. In 2011 Second International Conference on Intelligent Systems, Modelling and Simulation (pp. 136–138). IEEE.Google Scholar
  8. 8.
    Megersa, Y., & Alemu, G. (2015, September). Brain tumor detection and segmentation using hybrid intelligent algorithms. In AFRICON, 2015 (pp. 1–8). IEEE.Google Scholar
  9. 9.
    Badran, E. F., Mahmoud, E. G., & Hamdy, N. (2010, November). An algorithm for detecting brain tumors in MRI images. In Computer Engineering and Systems (ICCES), 2010 International Conference on (pp. 368–373). IEEE.Google Scholar
  10. 10.
    Amin, S. E., & Megeed, M. A. (2012, May). Brain tumor diagnosis systems based on artificial neural networks and segmentation using MRI. In Informatics and Systems (INFOS), 2012 8th International Conference on (pp. MM-119). IEEE.Google Scholar
  11. 11.
    Kharrat, A., Benamrane, N., Messaoud, M. B., & Abid, M. (2009, November). Detection of brain tumor in medical images. In Signals, Circuits and Systems (SCS), 2009 3rd International Conference on (pp. 1–6). IEEE.Google Scholar
  12. 12.
    Deshmukh, R. J., & Khule, R. S. (2014). Brain tumor detection using artificial neural network fuzzy inference system (ANFIS). International Journal of Computer Applications Technology and Research3(3), 150–154.Google Scholar
  13. 13.
    Dasgupta, A. (2012). Demarcation of brain tumor using modified fuzzy C-means. International Journal of Engineering Research and Applications2(4), 529–533.Google Scholar
  14. 14.
    Sharma, M., & Mukherjee, S. (2014). Fuzzy c-means, anfis and genetic algorithm for segmenting astrocytoma-a type of brain tumor. IAES International Journal of Artificial Intelligence3(1), 16.Google Scholar
  15. 15.
    Islam, S., & Ahmed, M. (2013). Implementation of image segmentation for natural images using clustering methods.Google Scholar
  16. 16.
    Abdel-Maksoud, E., Elmogy, M., & Al-Awadi, R. (2015). Brain tumor segmentation based on a hybrid clustering technique. Egyptian Informatics Journal16(1), 71–81.Google Scholar
  17. 17.
    MATLAB, User’s Guide, The Math Works.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Manorama Sharma
    • 1
    Email author
  • G. N. Purohit
    • 2
  • Saurabh Mukherjee
    • 2
  1. 1.Banasthali UniversityVanasthaliIndia
  2. 2.CSE DepartmentBanasthali UniversityVanasthaliIndia

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