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Evolutionary Intelligence

, Volume 4, Issue 1, pp 31–49 | Cite as

A hybrid harmony search algorithm for MRI brain segmentation

  • Osama Moh’d AliaEmail author
  • Rajeswari Mandava
  • Mohd Ezane Aziz
Special Issue

Abstract

Automatic magnetic resonance imaging (MRI) brain segmentation is a challenging problem that has received significant attention in the field of medical image processing. In this paper, we present a new dynamic clustering algorithm based on the hybridization of harmony search (HS) and fuzzy c-means to automatically segment MRI brain images in an intelligent manner. In our algorithm, the capability of standard HS is modified to automatically evolve the appropriate number of clusters as well as the locations of cluster centers. By incorporating the concept of variable length encoding in each harmony memory vector, this algorithm is able to represent variable numbers of candidate cluster centers at each iteration. A new HS operator, called the “empty operator”, has been introduced to support the selection of empty decision variables in the harmony memory vector. The PBMF cluster validity index is used as an objective function to validate the clustering result obtained from each harmony memory vector. Evaluation of the proposed algorithm has been performed using both real MRI data obtained from the Center for Morphometric Analysis at Massachusetts General Hospital and simulated MRI data generated using the McGill University BrainWeb MRI simulator. Experimental results show the ability of this algorithm to find the appropriate number of naturally occurring regions in brain images. Furthermore, the superiority of the proposed algorithm over various state-of-the-art segmentation algorithms is demonstrated quantitatively.

Keywords

Automatic brain MRI segmentation Dynamic fuzzy clustering Harmony search Fuzzy c-means PBMF index 

Notes

Acknowledgments

Many thanks to the anonymous reviewers for their valuable comments that helped to improve this paper. This research is supported by Universiti Sains Malaysia, USM’s fellowship scheme and 'Universiti Sains Malaysia Research University Grant’ grant titled 'Delineation and visualization of Tumour and Risk Structures—DVTRS' under grant number 1001 / PKOMP / 817001.

References

  1. 1.
    Dou W, Ruan S, Chen Y, Bloyet D, Constans JM (2007) A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images. Image Vis Comput 25(2):164–171CrossRefGoogle Scholar
  2. 2.
    Wang X, Gao XZ, Ovaska SJ (2008) A hybrid optimization method for fuzzy classification systems. In: Eighth international conference on hybrid intelligent systems,HIS ’08, pp 264–271Google Scholar
  3. 3.
    Liew AWC, Yan H (2006) Current methods in the automatic tissue segmentation of 3d magnetic resonance brain images. Curr Med Imaging Rev 2:91–103CrossRefGoogle Scholar
  4. 4.
    Wells WMI, Grimson WEL, Kikinis R, Jolesz FA (1996) Adaptive segmentation of mri data. IEEE Trans Med Imaging 15(4):429–442CrossRefGoogle Scholar
  5. 5.
    Kapur T, Grimson WEL, Wells WM, Kikinis R (1996) Segmentation of brain tissue from magnetic resonance images. Med Image Anal 1(2):109–127CrossRefGoogle Scholar
  6. 6.
    Zhou J, Rajapakse JC (2008) Fuzzy approach to incorporate hemodynamic variability and contextual information for detection of brain activation. Neurocomputing 71(16–18):3184–3192CrossRefGoogle Scholar
  7. 7.
    Szilagyi L, Benyo Z, Szilagyi SM, Adam HS (2003) MR brain image segmentation using an enhanced fuzzy c-means algorithm. In: Proceedings of the 25th annual international conference of the IEEE engineering in medicine and biology society, vol 1, pp 724–726Google Scholar
  8. 8.
    Mokbel HA, Morsy MES, Abou-Chadi FEZ (2000) Automatic segmentation and labeling of human brain tissue from MR images. In: Seventeenth national radio science conference, 17th NRSC’, pp 1–8Google Scholar
  9. 9.
    Xiaohe L, Taiyi Z, Zhan Q (2008) Image segmentation using fuzzy clustering with spatial constraints based on markov random field via bayesian theory. IEICE Trans Fundam Electron Commun Comput Sci E91-A(3):723–729Google Scholar
  10. 10.
    Van Leemput K, Maes F, Vandermeulen D, Suetens P (1999) Automated model-based bias field correction of MR images of the brain. IEEE Trans Med Imaging 18(10):885–896CrossRefGoogle Scholar
  11. 11.
    Van Leemput K, Maes F, Vandermeulen D, Suetens P (1999) Automated model-based tissue classification of MR images of the brain. IEEE Trans Med Imaging 18(10):897–908CrossRefGoogle Scholar
  12. 12.
    Bezdek JC, Hall LO, Clarke LP (1993) Review of MR image segmentation techniques using pattern recognition. Med Phys 20(4):1033–1048CrossRefGoogle Scholar
  13. 13.
    Chang YL, Li X (1994) Adaptive image region-growing. IEEE Trans Image Process 3(6):868–872CrossRefMathSciNetGoogle Scholar
  14. 14.
    Adams R, Bischof L (1994) Seeded region growing. IEEE Trans Pattern Anal Mach Intell 16(6):641–647CrossRefGoogle Scholar
  15. 15.
    Pohle R, Toennies KD (2001) Segmentation of medical images using adaptive region growing. In: Proceedings of SPIE (medical imaging), vol 4322, pp 1337–1346, San DiegoGoogle Scholar
  16. 16.
    Sijbers J, Scheunders P, Verhoye M, Van der Linden A, van Dyck D, Raman E (1997) Watershed-based segmentation of 3d MR data for volume quantization. Magn Reson Imaging 15(6):679–688CrossRefGoogle Scholar
  17. 17.
    Atkins MS, Mackiewich BT (1998) Fully automatic segmentation of the brain in MRI. IEEE Trans Med Imaging 17(1):98–107CrossRefGoogle Scholar
  18. 18.
    Ashtari M, Zito JL, Gold BI, Lieberman JA, Borenstein MT, Herman PG (1990) Computerized volume measurement of brain structure. Investig Radiol 25(7):798–805CrossRefGoogle Scholar
  19. 19.
    Ji L, Yan H (2002) Attractable snakes based on the greedy algorithm for contour extraction. Pattern Recognit 35(4):791–806zbMATHCrossRefGoogle Scholar
  20. 20.
    McInerney T, Terzopoulos D (1996) Deformable models in medical image analysis: a survey. Med Image Anal 1(2):91–108CrossRefGoogle Scholar
  21. 21.
    Zhou Y, Bai J (2007) Atlas-based fuzzy connectedness segmentation and intensity nonuniformity correction applied to brain mri. IEEE Trans Biomed Eng 54(1):122–129CrossRefGoogle Scholar
  22. 22.
    Clark MC, Hall LO, Goldgof DB, Velthuizen R, Murtagh FR, Silbiger MS (1998) Automatic tumor segmentation using knowledge-based techniques. IEEE Trans Med Imaging 17(2):187–201CrossRefGoogle Scholar
  23. 23.
    Clark M, Hall L, Goldgof D, Silbiger M (1997) Using fuzzy information in knowledge guided segmentation of brain tumors. In: Fuzzy logic in artificial intelligence towards intelligent systems, pp 167–181Google Scholar
  24. 24.
    Sonka M, Tadikonda SK, Collins SM (1996) Knowledge-based interpretation of MR brain images. IEEE Trans Med Imaging 15(4):443–452CrossRefGoogle Scholar
  25. 25.
    Shen S, Sandham W, Granat M, Sterr A (2005) MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE Trans Inf Technol Biomed 9(3):459–467CrossRefGoogle Scholar
  26. 26.
    Balafar M, Ramli A, Saripan M, Mashohor S (2010) Review of brain MRI image segmentation methods. Artif Intell Rev 33(3):261–274CrossRefGoogle Scholar
  27. 27.
    Withey D, Koles Z (2008) A review of medical image segmentation: methods and available software. Int J Bioelectromagn 10(3):125–148Google Scholar
  28. 28.
    Pham DL, Xu C, Prince JL (2000) Current methods in medical image segmentation. Annu Rev Biomed Eng 2(1):315–337CrossRefGoogle Scholar
  29. 29.
    Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Kluwer, DordrechtzbMATHGoogle Scholar
  30. 30.
    Hore P, Hall LO, Goldgof DB, Gu Y, Maudsley AA, Darkazanli A (2008) A scalable framework for segmenting magnetic resonance images. J Signal Process Syst 54(1–3):183–203CrossRefGoogle Scholar
  31. 31.
    Pham DL (1999) Statistical estimation and pattern recognition methods for robust segmentation of magnetic resonance images. PhD dissertation, The Johns Hopkins UniversityGoogle Scholar
  32. 32.
    Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T (2002) A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 21(3):193–199CrossRefGoogle Scholar
  33. 33.
    Zhang DQ, Chen SC (2004) A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artif Intell Med 32(1):37–50CrossRefGoogle Scholar
  34. 34.
    Liew AWC, Hong Y (2003) An adaptive spatial fuzzy clustering algorithm for 3-d MR image segmentation. IEEE Trans Med Imaging 22(9):1063–1075CrossRefGoogle Scholar
  35. 35.
    Chen S, Zhang D (2004) Robust image segmentation using fcm with spatial constraints based on new kernel-induced distance measure. IEEE Trans Syst Man Cybern Part B 34(4):1907–1916CrossRefGoogle Scholar
  36. 36.
    Chuang KS, Tzeng HL, Chen S, Wu J, Chen TJ (2006) Fuzzy c-means clustering with spatial information for image segmentation. Comput Med Imaging Graph 30(1):9–15CrossRefGoogle Scholar
  37. 37.
    Cai W, Chen S, Zhang D (2007) Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognit 40(3):825–838zbMATHCrossRefGoogle Scholar
  38. 38.
    Liao L, Lin T, Li B (2008) MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach. Pattern Recognit Lett 29(10):1580–1588CrossRefGoogle Scholar
  39. 39.
    Falkenauer E (1998) Genetic algorithms and grouping problems. Wiley, New YorkGoogle Scholar
  40. 40.
    Chiong R (2009) Nature-inspired algorithms for optimisation. Springer, BerlinCrossRefGoogle Scholar
  41. 41.
    Chiong R, Neri F, McKay R (2009) Nature that breeds solutions. In: Nature-inspired informatics for intelligent applications and knowledge discovery: implications in business, science and engineering. Information science reference, Hershey, PA, pp 1–24Google Scholar
  42. 42.
    Das S, Abraham A, Chakraborty UK, Konar A (2009) Differential evolution using a neighborhood-based mutation operator. IEEE Trans Evol Comput 13(3):526–553CrossRefGoogle Scholar
  43. 43.
    Saha S, Bandyopadhyay S (2009) A new line symmetry distance and its application to data clustering. J Comput Sci Technol 24(3):544–556CrossRefGoogle Scholar
  44. 44.
    Saha S, Bandyopadhyay S (2007) A fuzzy genetic clustering technique using a new symmetry based distance for automatic evolution of clusters. In: International conference on computing: theory and applications, ICCTA ’07, pp 309–314Google Scholar
  45. 45.
    Campello R, Hruschka E, Alves V (2009) On the efficiency of evolutionary fuzzy clustering. J Heuristics 15(1):43–75CrossRefGoogle Scholar
  46. 46.
    Pakhira MK, Bandyopadhyay S, Maulik U (2005) A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification. Fuzzy Sets Syst 155(2):191–214CrossRefMathSciNetGoogle Scholar
  47. 47.
    Maulik U, Bandyopadhyay S (2003) Fuzzy partitioning using a real-coded variable-length genetic algorithm for pixel classification. IEEE Trans Geosci Remote Sens 41(5):1075–1081CrossRefGoogle Scholar
  48. 48.
    Hruschka ER, Campello RJGB, Freitas AA, Carvalho ACPLFd (2009) A survey of evolutionary algorithms for clustering. IEEE Trans Syst Man Cybern Part C Appl Rev 39:133–155CrossRefGoogle Scholar
  49. 49.
    Horta D, Naldi M, Campello R, Hruschka E, de Carvalho A (2009) Evolutionary fuzzy clustering: an overview and efficiency issues. In: Foundations of computational intelligence, pp 167–195Google Scholar
  50. 50.
    Das S, Biswas A, Dasgupta S, Abraham A (2009) Bacterial foraging optimization algorithm: Theoretical foundations, analysis, and applications. In: Foundations of computational intelligence, pp 23–55Google Scholar
  51. 51.
    Alia OM, Mandava R, Ramachandram D, Aziz ME (2009) Dynamic fuzzy clustering using harmony search with application to image segmentation. In: IEEE international symposium on signal processing and information technology (ISSPIT09), pp 538–543Google Scholar
  52. 52.
    Geem ZW, Kim JH, Loganathan G (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68CrossRefGoogle Scholar
  53. 53.
    Geem Z, Choi J-Y (2007) Music composition using harmony search algorithm. In: Giacobini M (eds) Applications of evolutionary computing. Springer, Berlin, pp 593–600Google Scholar
  54. 54.
    Geem Z (2007) Harmony search algorithm for solving sudoku. In: Apolloni B, Howlett RJ, Jain L (eds) Knowledge-based intelligent information and engineering systems, ser. Lecture Notes in Computer Science, vol 4692. Springer, Berlin, pp 371–378Google Scholar
  55. 55.
    Geem ZW, Tseng C-L, Park Y (2005) Harmony search for generalized orienteering problem: Best touring in china. In: Wang L, Chen K, Ong Y (eds) Advances in natural computation. Springer, Berlin, pp 741–750CrossRefGoogle Scholar
  56. 56.
    Mahdavi M, Abolhassani H (2009) Harmony k-means algorithm for document clustering. Data Min Knowl Discov 18(3):370–391CrossRefGoogle Scholar
  57. 57.
    Geem ZW (2009) Harmony search algorithms for structural design optimization. Springer, BerlinCrossRefGoogle Scholar
  58. 58.
    Geem ZW (2009) Particle-swarm harmony search for water network design. Eng Optim 41(4):297–311CrossRefGoogle Scholar
  59. 59.
    Geem ZW, Lee KS, Park Y (2005) Application of harmony search to vehicle routing. Am J Appl Sci 2(12):1552–1557CrossRefGoogle Scholar
  60. 60.
    Geem Z (2007) Optimal scheduling of multiple dam system using harmony search algorithm. In: Computational and ambient intelligence. Springer, Berlin, pp 316–323Google Scholar
  61. 61.
    Ayvaz MT (2009) Application of harmony search algorithm to the solution of groundwater management models. Adv Water Resour 32(6):916–924CrossRefGoogle Scholar
  62. 62.
    Ayvaz MT (2007) Simultaneous determination of aquifer parameters and zone structures with fuzzy c-means clustering and meta-heuristic harmony search algorithm. Adv Water Resour 30(11):2326–2338CrossRefGoogle Scholar
  63. 63.
    Geem ZW, Williams JC (2008) Ecological optimization using harmony search. In: Proceedings of the American conference on applied mathematics, World Scientific and Engineering Academy and Society (WSEAS), Cambridge, MassachusettsGoogle Scholar
  64. 64.
    Vasebi A, Fesanghary M, Bathaee SMT (2007) Combined heat and power economic dispatch by harmony search algorithm. Int J Electr Power Energy Syst 29(10):713–719CrossRefGoogle Scholar
  65. 65.
    Fesanghary M, Damangir E, Soleimani I (2009) Design optimization of shell and tube heat exchangers using global sensitivity analysis and harmony search algorithm. Appl Therm Eng 29(5–6):1026–1031CrossRefGoogle Scholar
  66. 66.
    Geem ZW, Hwangbo H (2006) Application of harmony search to multi-objective optimization for satellite heat pipe design. In: Proceedings of US-Korea conference on science, technology, & entrepreneurship (UKC 2006), Teaneck, NJ, USA, Citeseer, pp 1–3Google Scholar
  67. 67.
    Panchal A (2009) Harmony search in therapeutic medical physics. In: Geem Z (eds) Music-inspired harmony search algorithm. Springer, Berlin, pp 189–203CrossRefGoogle Scholar
  68. 68.
    Alia OM, Mandava R, Ramachandram D, Aziz ME (2009) Harmony search-based cluster initialization for fuzzy c-means segmentation of mr images. In: TENCON 2009—2009 IEEE region 10 conference, pp 1–6Google Scholar
  69. 69.
    Al-Betar MA, Khader AT, Gani TA (2008) A harmony search algorithm for university course timetabling. In: The proceedings of the 7th international conference on the practice and theory of automated timetabling, Montreal, CanadaGoogle Scholar
  70. 70.
    Mohsen A, Khader A, Ramachandram D (2010) An optimization algorithm based on harmony search for rna secondary structure prediction. In: Geem Z (eds) Recent advances in harmony search algorithm. Springer, Berlin, pp 163–174CrossRefGoogle Scholar
  71. 71.
    Alia OM, Mandava R, Ramachandram D, Aziz ME (2009) A novel image segmentation algorithm based on harmony fuzzy search algorithm. In: International conference of soft computing and pattern recognition, 2009. SOCPAR ’09, pp 335–340Google Scholar
  72. 72.
    Ingram G, Zhang T (2009) Overview of applications and developments in the harmony search algorithm. In: Geem Z (eds) Music-inspired harmony search algorithm. Springer, Berlin, pp 15–37CrossRefGoogle Scholar
  73. 73.
    Geem ZW (2009) Music-inspired harmony search algorithm theory and applications. Springer, New YorkCrossRefGoogle Scholar
  74. 74.
    Alia OM, Mandava R, Aziz ME (2010) A hybrid harmony search algorithm to MRI brain segmentation. In: The 9th IEEE international conference on COGNITIVE INFORMATICS, ICCI2010., Tsinghua University, Beijing, China, IEEE, pp 712–719Google Scholar
  75. 75.
    IBSR: internet brain segmentation repository. Technical report, Massachusetts General Hospital, Center for Morphometric Analysis, Sep 2005 (online). Available: http://neuro-www.mgh.harvard.edu/cma/ibsr/
  76. 76.
    BainWeb: simulated brain database. Mcconnell Brain Imaging Centre. Montreal Neurological Institute, Mcgill University, Nov 2003 [online]. Available: http://www.bic.mni.mcgill.ca/brainweb
  77. 77.
    Geem Z (2010) State-of-the-art in the structure of harmony search algorithm. In: Geem Z (eds) Recent advances in harmony search algorithm. Springer, Berlin, pp 1–10CrossRefGoogle Scholar
  78. 78.
    Pakhira MK, Bandyopadhyay S, Maulik U (2004) Validity index for crisp and fuzzy clusters. Pattern Recognit 37(3):487–501zbMATHCrossRefGoogle Scholar
  79. 79.
    Al-Betar M, Khader A (2010) A harmony search algorithm for university course timetabling. Ann Oper Res 1–29Google Scholar
  80. 80.
    Ben-Hur A, Guyon I (2003) Detecting stable clusters using principal component analysis. Methods Mol Biol Then Totowa 224:159–182Google Scholar
  81. 81.
    Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc Ser B (Methodol) 39(1):1–38zbMATHMathSciNetGoogle Scholar
  82. 82.
    Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 13(8):841–847CrossRefGoogle Scholar
  83. 83.
    Peng Z (2006) Segmentation of white matter, gray matter, and CSF from MR brain images and extraction of vertebrae from MR spinal images. PhD thesis, Cincinnati, OHGoogle Scholar
  84. 84.
    Garcia-Sebastian M, Isabel Gonzalez A, Grana M (2009) An adaptive field rule for non-parametric MRI intensity inhomogeneity estimation algorithm. Neurocomputing 72(16–18):3556–3569CrossRefGoogle Scholar
  85. 85.
    Mayer A, Greenspan H (2009) An adaptive mean-shift framework for mri brain segmentation. IEEE Trans Med Imaging 28(8)Google Scholar
  86. 86.
    Jimenez-Alaniz JR, Medina-Banuelos V, Yanez-Suarez O (2006) Data-driven brain mri segmentation supported on edge confidence and a priori tissue information. IEEE Trans Med Imaging 25(1):74–83CrossRefGoogle Scholar
  87. 87.
    Marroquin JL, Vemuri BC, Botello S, Calderon E, Fernandez-Bouzas A, en Matematicas CI, Guanajuato M (2002) An accurate and efficient bayesian method for automatic segmentation of brain mri. IEEE Trans Med Imaging 21(8):934–945CrossRefGoogle Scholar
  88. 88.
    Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley Interscience, New YorkGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Osama Moh’d Alia
    • 1
    Email author
  • Rajeswari Mandava
    • 1
  • Mohd Ezane Aziz
    • 2
  1. 1.Computer Vision Research Group, School of Computer SciencesUniversity Sains MalaysiaPenangMalaysia
  2. 2.Department of Radiology-Health CampusUniversiti Sains MalaysiaKubang KerianMalaysia

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