Skip to main content

Advertisement

SpringerLink
Log in

Efficient Segmentation of Brain Tumor Using FL-SNM with a Metaheuristic Approach to Optimization

  • Patient Facing Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Nowadays, automatic tumor detection from brain images is extremely significant for many diagnostic as well as therapeutic purposes, due to the unpredictable shape and appearance of tumors. In medical image analysis, the automatic segmentation of tumors from brain using magnetic resonance imaging (MRI) data is the most critical issue. Existing research has some limitations, such as high processing time and lower accuracy, because of the time required for the training process. In this research, a new automatic segmentation process is introduced using machine learning and a swarm intelligence scheme. Here, a fuzzy logic with spiking neuron model (FL-SNM) is proposed for segmenting the brain tumor region in MR images. Initially, input images are preprocessed to remove Gaussian and Poisson noise using a modified Kuan filter (MKF). In the MKF, the optimal selection of the minimum MSE of image pixels is achieved using a random search algorithm (RSA), which improves the peak signal-to-noise ratio (PSNR). Then, the image is smoothed using an anisotropic diffusion filter (ADF) to reduce the over-filtering problem. Afterwards, to extract statistical texture features, Fisher’s linear-discriminant analysis (FLDA) is used. Finally, extracted features are transferred to the FL-SNM process and this scheme effectively segments the tumor region. In FL-SNM, the consequent parameters such as weight and bias play an important role in segmenting the region. Therefore, optimizing the weight parameter values using a chicken behavior-based swarm intelligence (CSI) algorithm, is proposed. The proposed (FL-SNM) scheme attained better performance in terms of high accuracy (94.87%), sensitivity (92.07%), specificity (99.34%), precision rate (89.36%), recall rate (88.39%), F-measure (95.06%), G-mean (95.63%), and DSC rate (91.2%), compared to existing convolutional neural networks (CNNs) and hierarchical self-organizing maps (HSOMs).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Dawngliana, M., Deb, D., Handique, M. and Roy, S., Automatic brain tumour segmentation in mri; hybridized multilevel thresholding and level set. International Symposium on Advanced Computing and Communication, 219–223, 2015.

  2. Zabir, I., Paul, S., Rayhan, M. A., Sarker, T., Fattah, S. A., and Shahnaz, C., Automatic brain tumor detection and segmentation from multi-modal MRI images based on region growing and level set evolution. IEEE International WIE Conference on Electrical and. Comput. Eng.:503–506, 2015.

  3. Cottet, G.-H., and Ayyadi, M. E. A., Volterra type model for image processing. IEEE Trans. Image Process. 7:292–303, 1998.

    Article  CAS  Google Scholar 

  4. Acharya, J., Gadhiya, S., and Raviya, K., Segmentation techniques for image analysis: a review. Int J ComputSci Manage Res 2(4):1218–1221, 2013.

    Google Scholar 

  5. Naik, D., and Shah, P., A review on image segmentation clustering algorithms. Int J ComputSci Inform Technol 5(3):3289–3293, 2014 ISSN: 0975-9646.

    Google Scholar 

  6. Christe, S. A., Malathy, K., and Kandaswamy, A., Improved hybrid segmentation of brain MRI tissue and tumor using statistical features. ICTACT J. Image Video Process 1(1):34–49, 2010.

    Article  Google Scholar 

  7. Seerha, G. K., and Kaur, R., Review on recent image segmentation techniques. Int J. ComputSci Eng (IJCSE) 5(2):109–112, 2013 ISSN: 0975-3397.

    Google Scholar 

  8. Liu, J. and Guo L., A New Brain MRI Image Segmentation Strategy Based on Wavelet Transform and K-means Clustering. IEEE International Conference on Signal Pro-cessing, Communications and Computing (ICSPCC), 1–4, 2015.

  9. Dass, R., and Priyanka Devi, S., Image segmentation techniques. Int J. Electron Commun Technol. 3(1):66–70, 2012 ISSN: 2230-7109 (Online).

    Google Scholar 

  10. Amin, S. A. and Megeed, M.A., Brain tumour diagnosis systems based on artificial neural networks and segmentation using MRI. The 8th International Conference on INFOmatics and systems, 119–124, 2012.

  11. Hiralal, R., and Menon, H. P., A survey of brain MRI image segmentation methods and the issues involved. Advances in Intelligent Systems and Computing 530:245–259, 2016. https://doi.org/10.1007/978-3-319-47952-1_19 Springer International Publishing.

    Article  Google Scholar 

  12. Si, T., De, A., and Bhattacharjee, A. K., Brain MRI segmentation for tumor detection via entropy maximization using Grammatical Swarm. Int. J. Wavelets Multiresolution Inf. Process. 13(5):1–8, 2015. https://doi.org/10.1142/S0219691315500393.

    Article  Google Scholar 

  13. Elsayad, A.M., Classification of breast cancer database using learning vector quantization neural networks. Saudi Association of Health Informatics, 1–9, 2014; https://www.researchgate.net/publication/242616752

  14. El-Sayed, A., El-Dahshan, E. S. A., Mohsen, H. M., Revett, K., and Salem, A. B. M., Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert Syst. Appl. 41:5526–5545, 2014. https://doi.org/10.1016/j.eswa.2014.01.021.

    Article  Google Scholar 

  15. Hyakin, S., Neural Networks and Learning Machines, 3rd Edition. Upper Saddle River: Pearson Prentice Hall, 2011.

    Google Scholar 

  16. Ortiz, A., Gorriz, J. M., Ramirez, J. and Salas-Gonzalez, D., Unsupervised Neural Techniques Applied to MR Brain Image Segmentation, Advances in Artificial Neural Systems. Hindawi Publishing Corporation. 457590:7, 2012. 10.1155/2012/457590.

  17. Zhang, Y., Dong, Z., Wua, L., and Wanga, S., A hybrid method for MRI brain image classification. Expert Syst. Appl. 38:10049–10053, 2011. https://doi.org/10.1016/j.eswa.2011.02.012.

    Article  Google Scholar 

  18. Liu, J., Li, M., Wang, J., Wu, F., Liu, T., and Pan, Y. A., Survey of MRI-Based Brain Tumour Segmentation Methods. Tsinghua Sci. Technol. 19:578–595, 2014.

    Article  CAS  Google Scholar 

  19. Sharma, M., and Mukharjee, S., Brain tumor segmentation using hybrid genetic algorithm and Artificial Neural Network Fuzzy Inference System (ANFIS). International Journal of Fuzzy Logic Systems (IJFLS) 2(4):31–42, 2012. https://doi.org/10.5121/ijfls.2012.240331.

  20. Logeswari, T., and Karnan, M., An enhanced implementation of brain tumor detection using segmentation based on soft computing, IACSIT'10. International Journal of Computer Theory and Engineering 2(4):1793–8201, 2010 586-590.

    Google Scholar 

  21. Pereira, S., Pinto, A., Alves, V., and Silva, C. A., Brain tumor segmentation using Convolutional Neural Networks in MRI images. IEEE Trans. Med. Imaging 35(5):1240–1251, 2016. https://doi.org/10.1109/TMI.2016.2538465.

    Article  PubMed  Google Scholar 

  22. Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., and Larochelle, H., Brain tumor segmentation with deep neural networks. Med. Image Anal. 35:18–31, 2017. https://doi.org/10.1016/j.media.2016.05.004.

    Article  PubMed  Google Scholar 

  23. Weickert, J., Anisotropic Diffusion in Image Processing. Stuttgart: BG. Teubner, 1998.

    Google Scholar 

  24. Ramos-Llordén, G., Vegas-Sánchez-Ferrero G, Martin-Fernandez M, Alberola-López C and Aja-Fernández S. Anisotropic diffusion filter with memory based on speckle statistics for ultrasound images. IEEE Trans. Image Process. 24(1):345–358, 2015.

    Article  Google Scholar 

  25. Shimabukuro, Y. E., and Smith, J. A., The least-squares mixing models to generate fraction images derived from remote sensing multispectral data. IEEE Trans on Geoscience and Remote Sensing 29(1):16–20, 1991.

    Article  Google Scholar 

  26. Gerstner, W., and Kistler, W. M., Spiking neuron models. Cambridge: Cambridge University Press, 2002.

    Book  Google Scholar 

  27. Meftah, B., Lezoray, O., Benyettou, A., Segmentation and Edge Detection Based on Spiking Neural Network Model Neural Process Lett published on august 20, 2010. https://doi.org/10.1007/s11063-010-9149-6.

  28. Hebb, D. O., The Organization of Behavior. New York: Wiley and Sons, 1949.

    Google Scholar 

  29. Meftah, B., Benyettou, A., Lezoray, O., and Debakla Image, M., Segmentation with Spiking Neuron Network, CP1019, Intelligent Systems and Automation, 1st Mediterranean conference. College Park: American Institute of Physics, 2008.

    Google Scholar 

  30. Zhang, Y., Wang, S., Ji, G., and Dong, Z., An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine. Hindawi Publishing Corporation. Sci. World J.:130134, 2013. https://doi.org/10.1155/2013/130134.

  31. Meng, X., Liu, Y., Gao, X. and Zhang, H., A new bio-inspired algorithm: chicken swarm optimization. In International Conference in Swarm Intelligence, 86–94. Springer International Publishing, 2014. https://doi.org/10.1007/978-3-319-11857-4_10.

  32. Zadeh, L. A., Fuzzy sets. Inf. Control. 8(3):338–353, 1965.

    Article  Google Scholar 

  33. Deng, Y. Y., and Dai, Q., Discriminative clustering and feature selection for brain MRI segmentation. IEEE Signal Processing Letters. 22:573–577, 2015. https://doi.org/10.1109/LSP.2014.2364612.

    Article  Google Scholar 

  34. Yang, M. S., Lin, K. C. R., Liu, H. C., and Lirng, J. F., Magnetic resonance imaging segmentation techniques using batch-type learning vector quantization algorithms. Magn. Reson. Imaging 25:265–277. Elsevier, 2007. https://doi.org/10.1016/j.mri.2006.09.043.

    Article  PubMed  Google Scholar 

  35. Song, T., Jamshidi, M. M., Lee, R. R., and Huang, M., A Modified Probabilistic Neural Network for Partial Volume Segmentation in Brain MR Image. IEEE Trans. on Neural Networks. 18(5):1424–1432, 2007. https://doi.org/10.1109/TNN.2007.891635.

    Article  PubMed  Google Scholar 

  36. Haralick, R. M., and Shanmugam, K., Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics. 3(6):610–621, 1973.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of EEE, SRS College of Engineering and Technology, Salem, India

    Aparna Natarajan

  2. Department of EEE, S. A. Engineering College, Chennai, India

    Sathiyasekar Kumarasamy

Authors
  1. Aparna Natarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sathiyasekar Kumarasamy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aparna Natarajan.

Ethics declarations

Conflict of Interest

The authors have no conflict of interest.

Human and animal rights

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

Ethical approval

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

Additional information

This article is part of the Topical Collection on Patient Facing Systems

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Natarajan, A., Kumarasamy, S. Efficient Segmentation of Brain Tumor Using FL-SNM with a Metaheuristic Approach to Optimization. J Med Syst 43, 25 (2019). https://doi.org/10.1007/s10916-018-1135-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-018-1135-y

Keywords

Search

Navigation