Skip to main content
SpringerLink
Log in

Adaptive weighted fuzzy region based optimization for brain MR image segmentation

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

Abstract

The recent trends in medical image segmentation and analysis are often used in many real world applications for analyzing different objects of interest. Analyzing the brain Magnetic Resonance (MR) images is found to be difficult task because of the existence of intensity non-uniformity. Although numerous models have been proposed to handle brain MR image segmentation, it is still a challenge to effectively approximate the Intensity Non-Uniformity (INU) and improve greater segmentation accuracy. Hence, an integrated energy minimization approach, namely adaptive weighted fuzzy region based optimization algorithm is developed for brain MR image segmentation. These adaptive fuzzy regions are iteratively weighted to estimate their membership values assigned to each pixel with respect to energy. Also, the optimal weighting parameter, membership values to each region, and bias fields are iteratively estimated and updated. Further, this algorithm is compared with the recent energy minimization approaches in simulated brain MR image dataset. The results of the quantitative evaluations demonstrate that the proposed algorithm gives more reliable segmentation and better accuracy in spite of initialization, noise, and intensity non-uniformity.

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

Similar content being viewed by others

References

  1. Chang H, Huang W, Wu C, Huang S, Guan C, Sekar S, Bhakoo KK, Duan Y (2016) A new Variational method for Bias correction and its applications to rodent brain extraction. IEEE Trans Med Imaging 13(9):1–14

    Google Scholar 

  2. Chen Y, Zhang J, Wang S, Zheng Y (2012) Brain magnetic resonance image segmentation based on an adapted non-local fuzzy c-means method. IET Comput Vis 6(6):610–625

    Article  MathSciNet  Google Scholar 

  3. Cocosco CA, Kollokian V, Kwan RK-S, Evans AC (1997) BrainWeb: online Interface to a 3D MRI simulated brain database. NeuroImage 5(4, part 2/4):S425 Proceedings of 3rd International Conference on Functional Mapping of the Human Brain, Copenhagen

    Google Scholar 

  4. Hou Z (2006) A review on MR image intensity inhomogeneity correction. Int J Biomed Imaging 2006:1–11

    Article  Google Scholar 

  5. Ji Z, Sun Q, Xia Y, Chen Q, Xia D, Feng D (2012) Generalized rough fuzzy c-means algorithm for brain MR image segmentation. Comput Methods Prog Biomed Elsevier 108:644–655

    Article  Google Scholar 

  6. Jijun REN, Yachong Zhang, Jun Che, Tao Wang, Long Shi, Liang Zhang (2012) A Novel Multiphase Level Set Method to Image Segmentation Combined with Edge Link method. Proc IEEE Int Conf Indust Info (INDIN)

  7. Krinidis S, Chatzis V (2010) A robust fuzzy local information C-means clustering algorithm. IEEE Trans Image Proc 19(5):1328–1337

    Article  MathSciNet  Google Scholar 

  8. Kumar S, Ray SK, Tewari P (2012) A hybrid approach for image segmentation using fuzzy clustering and level set method. Int J Image Graph Signal Proc 6:1–7

    Google Scholar 

  9. Kuo WF, Lin CY, Hsu WY (2011) Medical image segmentation using the combination of watershed and FCM clustering algorithms. Int J Innov Comput Info Control 7(9):5255–5267

    Google Scholar 

  10. Li C, Huang R, Zhaohua D, Chris Gatenby J, Metaxas DN, Gore JC (2011) A level set method for image segmentation in the presence of intensity Inhomogeneities with application to MRI. IEEE Trans Image Process 20(7):2007–2015

    Article  MathSciNet  Google Scholar 

  11. Li C, Gore JC, Davatzikos C (2014) Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magnet Reson Imaging Elsevier 32:913–923

    Article  Google Scholar 

  12. Likar B, Viergever M, Pernus F (2001) Retrospective correction of MR intensity inhomogeneity by information minimization. IEEE Trans Med Imaging 20(12):1398–1410

    Article  Google Scholar 

  13. Nathan Lowry, Rami Mangoubi, Mukund Desai, Youssef Marzouk and Paul Sammak (2011) A Unified Approach to Expectation-Maximization and Level Set Segmentation Applied To Stem Cell and Brain MRI Images. Proc IEEE Int Sympos Bio Med Imaging: Nano Macro

  14. Zhang Shi, She Lihuang, Lu Li, Zhong Hua (2013) A Modified Fuzzy C-Means for Bias Field Estimation and Segmentation of Brain MR Image. Proc IEEE Conf Control Dec Conf (CCDC)

  15. Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity non-uniformity in MRI data. IEEE Trans Med Imaging 17(1):87–97

    Article  Google Scholar 

  16. Tustison N, Avants B, Cook P, Zheng Y, Egan A, Yushkevich P, Gee J (2010) N4ITK: improved N3 Bias correction. IEEE Trans Med Imaging 29(6):1310–1320

    Article  Google Scholar 

  17. Vovk U, Pernus F, Likar B (2007) A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging 26(3):405–420

    Article  Google Scholar 

  18. Wang L, Li C, Sun Q, Xia D, Kao C-Y (2009) Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. Comput Med Imaging Graph 33:520–531

    Article  Google Scholar 

  19. Wang L, Shi F, Yap P-T, Lin W, Gilmore JH, Shen D (2013) Longitudinally guided level sets for consistent tissue segmentation of neonates. Human Brain Mapping Wiley 34(4):956–972

    Article  Google Scholar 

  20. Wang Y, Xiang S, Pan C, Wang L, Meng G (2013) Level set evolution with locally linear classification for image segmentation. Patt Recog Elsevier 46:1734–1746

    Article  Google Scholar 

  21. Bei Yan, Mei Xie, Jing-Jing Gao, Wei Zhao (2010) A Fuzzy C-means based algorithm for Bias Field Estimation and Segmentation of MR Images. Proc IEEE Int Conf Apperceiving Comput Intel Anal (ICACIA)

  22. Yang X, Fei B (2011) A multiscale and multiblock fuzzy C-means classification method for brain MR images. Med Phys Euro PMC 38(6):2879–2891

    Article  Google Scholar 

  23. Kaihua Zhang, Lei Zhang and Su Zhang (2010) A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction. Proceedings of IEEE International conference on Image processing

  24. Zhang H, Ye X, Chen Y (2013) An efficient algorithm for multiphase image segmentation with intensity Bias correction. IEEE Trans Image Process 22(10):3842–3851

    Article  MathSciNet  Google Scholar 

  25. Zhang K, Liu Q, Song H, Li X (2015) A Variational approach to simultaneous image segmentation and Bias correction. IEEE Trans Cybernet 45(8):1426–1437

    Article  Google Scholar 

  26. Zhao F, Jiao L, Liu H (2013) Kernel generalized fuzzy c-means clustering with spatial information for image segmentation. Digit Signal Proc Elsevier 23:184–199

    Article  MathSciNet  Google Scholar 

  27. Zheng Q, Dong E, Cao Z, Sun W, Li Z (2014) Active contour model driven by linear speed function for local segmentation with robust initialization and applications in MR brain images. Signal Proc Elsevier 97:117–133

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Misrimal Navajee Munoth Jain Engineering College, Affiliated to Anna University, Chennai, India

    Srinivasan Arulanandam

  2. Department of Computer Science Engineering, Jerusalem College of Engineering, Affiliated to Anna University, Chennai, India

    Sadagopan Selvarasu

Authors
  1. Srinivasan Arulanandam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sadagopan Selvarasu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Srinivasan Arulanandam.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arulanandam, S., Selvarasu, S. Adaptive weighted fuzzy region based optimization for brain MR image segmentation. Multimed Tools Appl 79, 3603–3621 (2020). https://doi.org/10.1007/s11042-018-6215-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6215-y

Keywords

Search

Navigation