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Metaheuristics for Medical Image Registration

  • Andrea Valsecchi
  • Enrique Bermejo
  • Sergio Damas
  • Oscar Cordón
Reference work entry

Abstract

In the last few decades, image registration (IR) has been a very active research area in computer vision. Applications of IR cover a broad range of real-world problems, including remote sensing, medical imaging, artificial vision, and computer-aided design. In particular, medical IR is a mature research field with theoretical support and two decades of practical experience. Formulated as either a continuous or combinatorial optimization problem, medical IR has been traditionally tackled by iterative numerical optimization methods, which are likely to get stuck in local optima and deliver suboptimal solutions. Recently, a large number of medical IR methods based on different metaheuristics, mostly belonging to evolutionary computation, have been proposed. In this chapter, we review the most recognized of these algorithms and develop an experimental comparison over real-world IR scenarios.

Keywords

Medical imaging Image registration Image segmentation 

Notes

Acknowledgements

This work is supported by the Spanish “Ministerio de Economía y Competitividad” under the NEWSOCO project (ref. TIN2015-53067661) and the Andalusian Department of Innovación, Ciencia y Empresa under project TIC2011-7745, both including European Regional Development Funds (ERDF).

References

  1. 1.
    Audette MA, Ferrie FP, Peters TM (2000) An algorithmic overview of surface registration techniques for medical imaging. Med Image Anal 4(3):201–217Google Scholar
  2. 2.
    Bäck T, Fogel DB, Michalewicz Z (1997) Handbook of evolutionary computation. IOP Publishing Ltd/Oxford University Press, BristolGoogle Scholar
  3. 3.
    Bermejo E, Cordón O, Damas S, Santamaría J (2013) Quality time-of-flight range imaging for feature-based registration using bacterial foraging. Appl Soft Comput 13(6):3178–3189Google Scholar
  4. 4.
    Bermejo E, Cordón O, Damas S, Santamaría J (2015) A comparative study on the application of advanced bacterial foraging models to image registration. Inform Sci 295:160–181Google Scholar
  5. 5.
    Besl PJ, McKay ND (1992) A method for registration of 3D shapes. IEEE Trans Pattern Anal Mach Intell 14:239–256Google Scholar
  6. 6.
    Beyer HG, Deb K (2001) On self-adaptive features in real-parameter evolutionary algorithms. IEEE Trans Evol Comput 5(3):250–270Google Scholar
  7. 7.
    Brunnström K, Stoddart A (1996) Genetic algorithms for free-form surface matching. In: International conference of pattern recognition, Vienna, pp 689–693Google Scholar
  8. 8.
    Chalermwat P, El-Ghazawi T, LeMoigne J (2001) 2-phase GA-based image registration on parallel clusters. Future Gener Comput Syst 17:467–476zbMATHCrossRefGoogle Scholar
  9. 9.
    Cordón O, Damas S (2006) Image registration with iterated local search. J Heuristics 12:73–94zbMATHCrossRefGoogle Scholar
  10. 10.
    Cordón O, Damas S, Santamaría J (2006) A fast and accurate approach for 3D image registration using the scatter search evolutionary algorithm. Pattern Recogn Lett 27(11): 1191–1200CrossRefGoogle Scholar
  11. 11.
    Cordón O, Damas S, Santamaría J (2006) Feature-based image registration by means of the CHC evolutionary algorithm. Image Vision Comput 22:525–533CrossRefGoogle Scholar
  12. 12.
    Cordón O, Damas S, Santamaría J, Martí R (2008) Scatter search for the 3D point matching problem in image registration. INFORMS J Comput 20:55–68MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Damas S, Cordón O, Santamaría J (2011) Medical image registration using evolutionary computation: an experimental survey. IEEE Comput Intell Mag 6(4):26–42CrossRefGoogle Scholar
  14. 14.
    De Falco I, Della Cioppa A, Maisto D, Tarantino E (2008) Differential evolution as a viable tool for satellite image registration. Appl Soft Comput 8(4):1453–1462zbMATHCrossRefGoogle Scholar
  15. 15.
    Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302CrossRefGoogle Scholar
  16. 16.
    Eshelman LJ (1993) Real-coded genetic algorithms and interval schemata. In: Whitley LD (ed) Foundations of genetic algorithms 2. Morgan Kaufmann, San Mateo, pp 187–202Google Scholar
  17. 17.
    Fitzpatrick J, Grefenstette J, Gucht D (1984) Image registration by genetic search. In: IEEE Southeast conference, Louisville, pp 460–464. EEUUGoogle Scholar
  18. 18.
    Fok K, Wong T, Wong M (2007) Evolutionary computing on consumer graphics hardware. IEEE Intell Syst 22(2):69–78CrossRefGoogle Scholar
  19. 19.
    Glover F, Kochenberger GA (eds) (2003) Handbook of metaheuristics. Kluwer Academic Publishers, BostonzbMATHGoogle Scholar
  20. 20.
    Glover F, Laguna M, Martí R (2003) Scatter search. In: Ghosh A, Tsutsui S (eds) Advances in evolutionary computation: theory and applications. Springer, New York, pp 519–537Google Scholar
  21. 21.
    Goldberg DE (1989) Genetic algoritms in search and optimization. Addison-Wesley, New York. EEUUGoogle Scholar
  22. 22.
    Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann ArborGoogle Scholar
  23. 23.
    Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5(2):143–156CrossRefGoogle Scholar
  24. 24.
    Kennedy J, Eberhart R (2001) Swarm intelligence. Morgan Kaufmann, San FranciscoGoogle Scholar
  25. 25.
    Klein S, Staring M, Pluim JPW (2007) Evaluation of optimization methods for nonrigid medical image registration using mutual information and b-splines. IEEE Trans Image Process 16(12):2879–2890MathSciNetCrossRefGoogle Scholar
  26. 26.
    Klein S, Pluim J, Staring M, Viergever M (2009) Adaptive stochastic gradient descent optimisation for image registration. Int J Comput Vis 81:227–239CrossRefGoogle Scholar
  27. 27.
    Kwan RKS, Evans AC, Pike GB (1999) MRI simulation-based evaluation of image-processing and classification methods. IEEE Trans Med Imaging 18(11):1085–1097CrossRefGoogle Scholar
  28. 28.
    Laguna M, Martí R (2003) Scatter search: methodology and implementations in C. Kluwer Academic Publishers, BostonzbMATHCrossRefGoogle Scholar
  29. 29.
    Liu Y (2004) Improving ICP with easy implementation for free form surface matching. Pattern Recognit 37(2):211–226zbMATHMathSciNetCrossRefGoogle Scholar
  30. 30.
    Lomonosov E, Chetverikov D, Ekart A (2006) Pre-registration of arbitrarily oriented 3D surfaces using a genetic algorithm. Pattern Recogn Lett 27(11):1201–1208CrossRefGoogle Scholar
  31. 31.
    Lozano M, Herrera F, Krasnogor N, Molina D (2004) Real-coded memetic algorithms with crossover hill-climbing. Evolut Comput 12(3):273–302CrossRefGoogle Scholar
  32. 32.
    Maes F, Vandermeulen D, Suetens P (1999) Comparative evaluation of multiresolution optimization strategies for image registration by maximization of mutual information. Med Image Anal 3(4):373–386CrossRefGoogle Scholar
  33. 33.
    Mandava VR, Fitzpatrick JM, Pickens DR (1989) Adaptive search space scaling in digital image registration. IEEE Trans Med Imaging 8(3):251–262CrossRefGoogle Scholar
  34. 34.
    Mesejo P, Valsecchi A, Marrakchi-Kacem L, Cagnoni S, Damas S (2015) Biomedical image segmentation using geometric deformable models and metaheuristics. Comput Med Imaging Graph 43:167–178CrossRefGoogle Scholar
  35. 35.
    Monga O, Deriche R, Malandain G, Cocquerez JP (1991) Recursive filtering and edge tracking: two primary tools for 3D edge detection. Image Vision Comput 9(4):203–214CrossRefGoogle Scholar
  36. 36.
    Ong YS, Lim M, Zhu N, Wong K (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern 36(1):141–152CrossRefGoogle Scholar
  37. 37.
    Ong YS, Lim MH, Chen X (2010) Memetic computation – past, present & future. IEEE Comput Intell Mag 5(2):24–31CrossRefGoogle Scholar
  38. 38.
    Passino K (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst 22(3):52–67MathSciNetCrossRefGoogle Scholar
  39. 39.
    Pluim JPW, Maintz JBA, Viergever MA (2003) Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imaging 22(8):986–1004CrossRefGoogle Scholar
  40. 40.
    Poupon C, Poupon F, Allirol L, Mangin JF (2006) A database dedicated to anatomo-functional study of human brain connectivity. In: 12th annual meeting of the organization for human brain mapping, Florence, vol 646Google Scholar
  41. 41.
    Powell MJD (1964) An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput J 7(2):155–162MathSciNetzbMATHCrossRefGoogle Scholar
  42. 42.
    Robertson C, Fisher RB (2002) Parallel evolutionary registration of range data. Comput Vis Image Underst 87:39–50zbMATHCrossRefGoogle Scholar
  43. 43.
    Rouet JM, Jacq JJ, Roux C (2000) Genetic algorithms for a robust 3-D MR-CT registration. IEEE Trans Inf Technol Biomed 4(2):126–136CrossRefGoogle Scholar
  44. 44.
    Rusinkiewicz S, Levoy M (2001) Efficient variants of the ICP algorithm. In: Third international conference on 3D digital imaging and modeling (3DIM 2001), Quebec, pp 145–152Google Scholar
  45. 45.
    Santamaría J, Cordón O, Damas S, García-Torres J, Quirin A (2009) Performance evaluation of memetic approaches in 3D reconstruction of forensic objects. Soft Comput 13(8–9):883–904CrossRefGoogle Scholar
  46. 46.
    Solis FJ, Wets RJB (1981) Minimization by random search techniques. Math Oper Res 6(1):19–30MathSciNetzbMATHCrossRefGoogle Scholar
  47. 47.
    Storn R (1997) Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359MathSciNetzbMATHCrossRefGoogle Scholar
  48. 48.
    Svedlow M, Mc-Gillem CD, Anuta PE (1976) Experimental examination of similarity measures and preprocessing methods used for image registration. In: Swain P, Morrison D, Parks D (eds) Symposium on machine processing of remotely sensed data, Indiana, vol 4(A), pp 9–17Google Scholar
  49. 49.
    Tsang PWM (1997) A genetic algorithm for aligning object shapes. Image Vis Comput 15: 819–831CrossRefGoogle Scholar
  50. 50.
    Valsecchi A, Damas S, Santamaría J, Marrakchi-Kacem L (2013) Genetic algorithms for voxel-based medical image registration. In: IEEE fourth international workshop on computational intelligence in medical imaging (CIMI 2013), pp 22–29Google Scholar
  51. 51.
    Valsecchi A, Dubois-Lacoste J, Stützle T, Damas S, Santamaría J, Marrakchi-Kacem L (2013) IEEE congress on evolutionary medical image registration using automatic parameter tuning. In: Evolutionary computation (CEC 2013), pp 1326–1333Google Scholar
  52. 52.
    Valsecchi A, Damas S, Santamaría J (2014) Evolutionary intensity-based medical image registration: a review. Curr Med Imaging Rev 10:283–297CrossRefGoogle Scholar
  53. 53.
    Valsecchi A, Damas S, Santamaría J, Marrakchi-Kacem L (2014) Intensity-based image registration using scatter search. Artif Intell Med 60(3):151–163CrossRefGoogle Scholar
  54. 54.
    Vemuri BC, Ye J, Chen Y, Leonard CM (2003) Image registration via level-set motion: applications to atlas-based segmentation. Med Image Anal 7(1):1–20CrossRefGoogle Scholar
  55. 55.
    Wachowiak MP, Smolikova R, Zheng Y, Zurada JM, El-Maghraby AS (2004) An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Trans Evol Comput 8(3):289–301CrossRefGoogle Scholar
  56. 56.
    Wang XY, Eberl S, Fulham M, Som S, Feng DD (2008) Data registration and fusion. In: Feng DD (ed) Biomedical information technology. Academic Press, Burlingto, pp 187–210CrossRefGoogle Scholar
  57. 57.
    Yamany SM, Ahmed MN, Farag AA (1999) A new genetic-based technique for matching 3D curves and surfaces. Pattern Recognit 32:1817–1820CrossRefGoogle Scholar
  58. 58.
    Zambanini S, Sablatnig R, Maier H, Langs Gd (2010) Automatic image-based assessment of lesion development during hemangioma follow-up examinations. Artif Intell Med 50(2):83–94CrossRefGoogle Scholar
  59. 59.
    Zhang Z (1994) Iterative point matching for registration of free-form curves and surfaces. Int J Comput Vis 13(2):119–152CrossRefGoogle Scholar
  60. 60.
    Zitová B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21: 977–1000CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Andrea Valsecchi
    • 1
  • Enrique Bermejo
    • 1
  • Sergio Damas
    • 2
  • Oscar Cordón
    • 1
  1. 1.Department of Computer Science and Artificial IntelligenceUnviersity of GranadaGranadaSpain
  2. 2.Department Software EngineeringUniversity of GranadaGranadaSpain

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