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An improved brain MR image binarization method as a preprocessing for abnormality detection and features extraction

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Abstract

This paper propose a computerized method of magnetic resonance imaging (MRI) of brain binarization for the uses of preprocessing of features extraction and brain abnormality identification. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to extensive black background or large variation in contrast between background and foreground of MRI. We have proposed a binarization that uses mean, variance, standard deviation and entropy to determine a threshold value followed by a non-gamut enhancement which can overcome the binarization problem of brain component. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error. A comparison is carried out among the obtained outcome with this innovative method with respect to other well-known methods.

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References

  1. Khurshid K, Siddiqi I, Faure C, Vincent N. Comparison of Niblack inspired binarization methods for ancient documents. In: Proceedings of IS&T/SPIE Electronic Imaging. 2009

    Google Scholar 

  2. Sezgin M, Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 2004, 13(1): 146–168

    Article  Google Scholar 

  3. Roy S, Dey A, Chatterjee K, Bandyopadhyay S K. An efficient binarization method for MRI of brain image. Signal & Image Processing: An International Journal, 2012, 3(6): 35–51

    Google Scholar 

  4. Otsu N. A threshold selection method from gray level histograms. IEEE Transactions on System, Man and Cybernetics, 1979, 9(1): 62–66

    Article  Google Scholar 

  5. Ridler TW, Calvard S. Picture thresholding using an iterative selection method. IEEE Transactions on System, Man and Cybernetics, 1978, 8(8): 630–632

    Article  Google Scholar 

  6. Kapur J N, Sahoo P K, Wong A K C. A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision, Graphics, and Image Processing, 1985, 29(3): 273–285

    Article  Google Scholar 

  7. Niblack W. An Introduction to Digital Image Processing. 2nd ed. Upper Saddle River: Prentice Hall, 1986

    Google Scholar 

  8. Sauvola J, Pietikäinen M. Adaptive document image binarization. Pattern Recognition, 2000, 33(2): 225–236

    Article  Google Scholar 

  9. Oh W, Lindquist B. Image thresholding by indicator kriging. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(7): 590–602

    Article  Google Scholar 

  10. Chen T, Takagi M. Image binarization by back propagation algorithm. International Archives of Photogrammetry and Remote Sensing, 1993, 29: 345–349

    Google Scholar 

  11. Roy S, Saha S, Dey A, Shaikh S H, Chaki N. Performance evaluation of multiple image binarization algorithms using multiple metrics on standard image databases. In: Proceedings of the 48th Annual Convention of Computer Society of India-Vol II. 2014, 349–360

    Google Scholar 

  12. Sund T, Eilertsen K. An algorithm for fast adaptive image binarization with applications in radiotherapy imaging. IEEE Transactions on Medical Imaging, 2003, 22(1): 22–28

    Article  Google Scholar 

  13. Rodríguez R. Binarization of medical images based on the recursive application of mean shift filtering: another algorithm. Advances and Applications in Bioinformatics and Chemistry, 2008, 1: 1–12

    Article  Google Scholar 

  14. Zhu Z H, Xu Y H, Gao Z, Hong W X. Attribute partial order trees for medical image analysis. Journal of Convergence Information Technology, 2013, 8(3)

    Google Scholar 

  15. Gal Y, Mehnert A, Rose S, Crozier S. Mutual information based binarization of multiple images of an object: an application in medical imaging. IET Computer Vision, 2013, 7(3): 163–169

    Article  Google Scholar 

  16. Senthilkumaran N, Kirubakaran C. Efficient implementation of Niblack thresholding for MRI brain image segmentation. International Journal of Computer Science and Information Technologies, 2014, 5: 2173–2176

    Google Scholar 

  17. Emaan S, Babu A R. An efficient segmentation for medical images based on iterative tri class thresholding technique. International Journal of Science, Engineering and Technology Research, 2015, 4(16): 3052–3055

    Google Scholar 

  18. Woo Y W. Performance evaluation of binarizations of scanned insect footprints. In: Proceedings of International Workshop on Combinatorial Image Analysis. 2004, 669–678

    Chapter  Google Scholar 

  19. Murtaza S M, Ahmad J, Ali U. Efficient generalized colored image enhancement. In: Proceedings of IEEE Conference on Cybernetics and Intelligent Systems. 2006, 1–5

    Google Scholar 

  20. Roy S, Nag S, Bandyopadhyay S K, Bhattacharaya D, Kim T H. Automated brain hemorrhage lesion segmentation and classification from MR image using an innovative composite method. Journal of Theoretical and Applied Information Technology, 2015, 78(1): 34–45

    Google Scholar 

  21. Roy S, Ghosh P, Bandyopadhyay S K. Segmentation and contour extraction of cerebral hemorrhage from MRI of brain by gamma transformation approach. In: Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications. 2014

    Google Scholar 

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Acknowledgements

It is our privilege to thank Dr. Pradip Saha, who received his MD in Radiology from NRS Medical College and was an ex- Faculty member there. He is currently a radiologist at M N Roy Diagnostic Center, Kolkata, India. He is helpful for reference image creation, guidance and the valuable suggestions to complete this paper.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Institute of Computer Technology (UVPCE), Ganpat University, Ahmadabad, 380060, India

    Sudipta Roy

  2. Information Technology Department, College of Engineering, Bharati Vidyapeeth deemed University, Pune, 411043, India

    Debnath Bhattacharyya

  3. Department of Computer Science and Engineering, University of Calcutta, Technology Campus, Kolkata, 700098, India

    Samir Kumar Bandyopadhyay

  4. Department of Convergence Security, Sungshin Women’s University, Seoul 136-742, Korea

    Tai-Hoon Kim

Authors
  1. Sudipta Roy
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  2. Debnath Bhattacharyya
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  3. Samir Kumar Bandyopadhyay
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  4. Tai-Hoon Kim
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Corresponding author

Correspondence to Sudipta Roy.

Additional information

Sudipta Roy is pursuing the PhD degree in the field of image processing from Department of Computer Science and Engineering, University of Calcutta (CU), India from 2014. He received his B.Tech and M.Tech degrees from CU in 2011 and 2013, respectively. He is author of more than 25 publications in National and International Journal and conferences including IEEE, Springer, Elsevier, etc. He is a reviewer of many journals including IET image processing, European Journal of Medical Physics, Computer in Biology and Medicine, Elsevier, and conferences including C3IT-2015, INDIACom-2015, CSI-2014, 3rd FICTA 2014 and so on. He is an International Advisory Committee member of many conferences. He is currently working as an assistant professor in the Department of Computer Science and Engineering, Academy of Technology, India.

Debnath Bhattacharyya received the PhD degree in the Department of Computer Science and Engineering, University of Calcutta, India and M.Tech degree in the Department of Computer Science and Engineering, West Bengal University of Technology, India. Currently, he is associated as a professor with IT Department at College of Engineering, Bharati Vidyapeeth University, India. He has 18 years of experience in teaching and researching. His research interests include bioinformatics, image processing and pattern recognition. He has published 145 research papers in international journals and conferences and four text books on computer science.

Samir Kumar Bandyopadhyay is currently a professor of Computer Science & Engineering, University of Calcutta, India. He is a visiting faculty in the Department of Computer Science, Southern Illinois University, USA; MIT, USA; California Institute of Technology, USA, etc. He is a chairman of SERSC; Indian Part, a fellow of Computer Society of India; the sectional president of ICT of Indian Science Congress Association; a senior member of IEEE, etc. He has published 300 research papers in international & Indian journals and five leading text books on computer science and engineering.

Tai-Hoon Kimreceived his BE, and ME degrees from Sungkyunkwan University, Korea and PhD degree from University of Bristol, UK and University of Tasmania, Australia. Now he is working in the Department of Convergence Security, Sungshin W. University, Korea. His main research areas are security engineering for IT products, IT systems, development processes, and operational environments. He published 300 research papers in international journals and conferences. He is the editor of Elsevier, Springer, etc.

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Roy, S., Bhattacharyya, D., Bandyopadhyay, S.K. et al. An improved brain MR image binarization method as a preprocessing for abnormality detection and features extraction. Front. Comput. Sci. 11, 717–727 (2017). https://doi.org/10.1007/s11704-016-5129-y

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