Skip to main content
SpringerLink
Log in

An optimized SVM based possibilistic fuzzy c-means clustering algorithm for tumor segmentation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

To design an efficient partial differential equation-based total variation method for denoising and possibilistic fuzzy c-means clustering algorithm for segmentation and these methods presented the more detailed information of the MRI medical images compared to traditional methods. In this article, the pipeline of the proposed method described by two modules like pre-processing and segmentation. In pre-processing, noisy image is decomposed using nonsubsampled contourlet transform and it contains highpass contourlet coefficient (i.e., noisy coefficient) is removed by the threshold method as well. After reconstruction, the primary denoised image is enhanced by an improved partial differential equation-based total variation method in terms of image details like edges, boundaries, etc. In segmentation, the enhanced primary denoised image is segmented by an improved possibilistic fuzzy c-means clustering algorithm that avoids limitations in possibilistic c-means, fuzzy c-means, and K-means clustering. Next, a support vector machine classifier is utilized to identify brain tissues into gray matter, white matter, cerebrospinal fluid, and tumor part. The parameters were optimally selected by a grey wolf optimization algorithm for the classification of brain tissues. The performance of the proposed method is computed with reference to peak signal-to-noise ratio, mean square error, structural similarity index, sensitivity, specificity, and accuracy. The experimental results claimed that the proposed method is better than the traditional methods.

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
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Ananthi VP, Balasubramaniam P, Kalaiselvi T (2016) A new fuzzy clustering algorithm for the segmentation of brain tumor. Softw Comput 20(12):4859–4879

    Article  Google Scholar 

  2. Bahadure NB, Ray AK, Thethi HP (2017) Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int J Biomed Imaging 2017:1–12

    Article  Google Scholar 

  3. BRATS2018 database: https://www.med.upenn.edu/sbia/brats2018/data.html

  4. Chang SG, Vetterli M (2000) Adaptive wavelet thresholding for image denoising and compression. IEEE Trans Imaging Process 9(9):1532–1546

    Article  MathSciNet  MATH  Google Scholar 

  5. Cunha AL, Do MN (2006) The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans Imaging Process 15(10):3089–3101

    Article  Google Scholar 

  6. Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Imaging Process 14 (12):2091–2106

    Article  Google Scholar 

  7. Donoho DL (1995) De-noising by soft-thresholding. IEEE Trans Inform Theory 41(3):613–627

    Article  MathSciNet  MATH  Google Scholar 

  8. Dubey YK, Mushrif MM, Mitra K (2016) Segmentation of brain MR images using rough set based intuitionistic fuzzy clustering. Biocybern Biomed Eng 36(2):413–426

    Article  Google Scholar 

  9. Eswaramoorthy S, Sivakumaran N, Sekaran S (2016) Grey wolf optimization-based parameter selection for support vector machines. COMPEL—Int J Comput Math Electr Electron Eng 35(5):1513–1523

    Article  Google Scholar 

  10. Hajiaboli MR (2011) An anisotropic fourth-order diffusion filter for image noise removal. Int J Comput Vis 92(2):177–191

    Article  MathSciNet  MATH  Google Scholar 

  11. Herald ARN, Selvathi D (2018) Performance analysis of brain tissues and tumor detection and grading system using ANFIS classifier. Int J Imaging Syst Technol 28(2):77–85

    Article  Google Scholar 

  12. Irum I, Shahid MA, Raza M (2015) A review of image denoising methods. J Eng Sci Technol Rev 8(5):41–48

    Article  Google Scholar 

  13. Ji ZX, Sun QS, Xia DS (2011) A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image. Comput Med Imaging Graph 35(5):383–397

    Article  Google Scholar 

  14. Kalaiselvi T, Sriramakrishnan P (2018) Rapid brain tissue segmentation process by modified FCM algorithm with CUDA enabled GPU machine. Int J Imaging Syst Technol 28(3):163–174

    Article  Google Scholar 

  15. Kollem S, Reddy KRL, Rao DS (2019) Denoising and segmentation of MR images using fourth-order non-linear adaptive PDE and new convergent clustering. Int J Imaging Syst Technol 29(3):195–209

    Article  Google Scholar 

  16. Kollem S, Reddy KRL, Rao DS (2020) Improved partial differential equation-based total variation approach to nonsubsampled contourlet transform for medical image denoising. Multimed Tools Appl: 1–26. https://doi.org/10.1007/s11042-020-09745-1

  17. Kollem S, Reddy KRL, Rao DS (2020) Modified transform-based gamma correction for MRI malignant image denoising and tumor segmentation by optimized Histon based elephant herding algorithm. Int J Imaging Syst Technol: 1–23. https://doi.org/10.1002/ima.22429

  18. Korti A (2018) Regularization in parallel magnetic resonance imaging. Int J Imaging Syst Technol 28(2):92–98

    Article  Google Scholar 

  19. Krishnapuram R, Keller JM (1993) A possibilistic approach to clustering. IEEE Trans Fuzzy Syst 1(2):98–110

    Article  Google Scholar 

  20. Liu X, Jia M, Zhang X, Lu W (2018) A novel multichannel Internet of things based on dynamic spectrum sharing in 5G communication. IEEE Int Things J 6(4):5962–5970

    Article  Google Scholar 

  21. Liu X, Zhang X (2019) NOMA-based resource allocation for cluster-based cognitive industrial Internet of things. IEEE Trans Indust Inform 16(8):5379–5388

    Article  Google Scholar 

  22. Magnin B, Mesrob L, Benali H (2009) Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI. Neuroradiology 51(2):73–83

    Article  Google Scholar 

  23. Meng XY, Che L (2017) Towards a partial differential equation remote sensing image method based on the adaptive degradation diffusion parameter. Multimed Tools Appl 76(17):17651–17667

    Article  Google Scholar 

  24. Mirjalili S, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  25. Namburu A, Kumar Samay S, Edara SR (2017) Soft fuzzy rough set-based MR brain image segmentation. Appl Softw Comput 54:456–466

    Article  Google Scholar 

  26. Nnolim UA (2017) FPGA-based hardware architecture for fuzzy homomorphic enhancement based on partial differential equations. Int J Imaging Graph 17(4):1–46

    Google Scholar 

  27. Phophalia A, Mitra SK (2015) Rough set based bilateral filter design for denoising brain MR images. Appl Softw Comput 33:1–14

    Article  Google Scholar 

  28. Rajaguru H, Ganesan K, Bojan VK (2016) Earlier detection of cancer regions from MR image features and SVM classifiers. Int J Imaging Syst Technol 26(3):196–208

    Article  Google Scholar 

  29. Roy PK, Mandal D (2014) Oppositional biogeography-based optimisation for optimal power flow. Int J Power Eng Convert 5(1):47–69

    Article  Google Scholar 

  30. Sam BB, Fred AL (2018) An efficient grey wolf optimization algorithm based extended kalman filtering technique for various image modalities restoration process. Multimed Tools Appl 77(23):30205–30232

    Article  Google Scholar 

  31. Tian C, Chen Y (2019) Image segmentation and denoising algorithm based on partial differential equations. IEEE Sens J: 1–8. https://doi.org/10.1109/JSEN.2019.2959704

  32. Wang D, Gao J (2015) An improved noise removal model based on nonlinear fourth-order partial differential equations. Int J Compr Math 93(6):942–954

    Article  MathSciNet  MATH  Google Scholar 

  33. Zhang C (2016) Image denoising by using PDE and GCV in tetrolet transform domain. Eng Appl Artif Intell 48:204–229

    Article  Google Scholar 

  34. Zhao X, Zhang Y, Fan Y (2018) A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med Image Anal 43:98–111

    Article  Google Scholar 

Download references

Acknowledgements

The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.

Author information

Authors and Affiliations

  1. Department of ECE, School of Engineering, SR University, Warangal, 506371, Telangana, India

    Sreedhar Kollem

  2. Department of ETM, G. Narayanamma Institute of Technology and Science, Hyderabad, 500104, Telangana, India

    Katta Ramalinga Reddy

  3. Department of ECE, JNTUH College of Engineering, Kukatpally, Hyderabad, 500085, Telangana, India

    Duggirala Srinivasa Rao

  4. Research Scholar, Department of ECE, JNTUH University, Hyderabad, 500085, Telangana, India

    Sreedhar Kollem

Authors
  1. Sreedhar Kollem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Katta Ramalinga Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Duggirala Srinivasa Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sreedhar Kollem.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kollem, S., Reddy, K.R. & Rao, D.S. An optimized SVM based possibilistic fuzzy c-means clustering algorithm for tumor segmentation. Multimed Tools Appl 80, 409–437 (2021). https://doi.org/10.1007/s11042-020-09675-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09675-y

Keywords

Search

Navigation