Immune-Based Feature Selection in Rigid Medical Image Registration Using Supervised Neural Network

  • Joydev Hazra
  • Aditi Roy Chowdhury
  • Paramartha Dutta
Part of the Studies in Computational Intelligence book series (SCI, volume 611)


Different radiological images like computed tomography (CT) and magnetic resonance (MR) are increasingly being used in medical science research for diagnosis and treatment. This article presents an automatic image registration technique to register MR–MR images using gray-level co-occurrence matrix (GLCM) and neural networks. This technique identifies different features of a brain image and its transformational counterpart. GLCM-based image feature extraction is a co-occurrence-based method by which different feature parameters are obtained. These parameters are calculated from the co-occurrence matrix along four directions, namely 0°, 45°, 90°, and 135°. Six features are selected from a set of features using an artificial immune system-based optimized feature selection technique and these six parameters are fed into the proposed neural network. Based on the principle of backpropagation algorithm, transformation parameters between the referenced and the sensed images are estimated. To demonstrate the effectiveness of the proposed method, experiment is carried out on MR T1, T2 datasets, and the results are compared with two other existing medical image registration techniques. The proposed method shows convincing results compared to others with respect to the estimation of underlying transformation parameters.


Image registration GLCM Artificial immune system Feature extraction Backpropagation 


  1. 1.
    Goshtaby A (2005) 2-D and 3-D image registration for medical, remote sensing, and industrial applications. Wiley, New JersyGoogle Scholar
  2. 2.
    Collins DL, Neelin P, Peters TM, Evans AC (1994) Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assis Tomog 18(2):192–205Google Scholar
  3. 3.
    Gee J, Reivich M, Bajacsy R (1993) Elastically deforming 3D atlas to match anatomical brain images. J Comput Assist Tomogr 17(2):225–236CrossRefGoogle Scholar
  4. 4.
    Bajcsy R, Kovacic S (1989) Multiresolution elastic matching. Comput Vision Graphics Image Process 46(1):1–21CrossRefGoogle Scholar
  5. 5.
    Christensen GE, Rabitt RD, Miller MI (1996) Deformable templates using large deformation kinematics. IEEE Trans Med Imag 5(10):1435–1447CrossRefGoogle Scholar
  6. 6.
    Christensen GE (1996) Individualizing neuroanatomical atlases using a massively parallel computer. IEEE Comput 29(1):32–38CrossRefGoogle Scholar
  7. 7.
    Bookstein FL (1989) Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans Pattern Anal Mach Intell 11(6):567–585CrossRefMATHGoogle Scholar
  8. 8.
    Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ (1999) Non-rigid registration using free-form deformation: application to breast MR images. IEEE Trans Med Imag 18(8):712–721CrossRefGoogle Scholar
  9. 9.
    Habboush IH, Mitchell KD, Mulkern RV, Barnes PD, Treves ST (1996) Registration and alignment of three-dimensional images: an interactive visual approach. Radiology 199(2):573–578Google Scholar
  10. 10.
    Maurer CR, Fitzpatrick JM, Wang MY, Galloway RL, Maciunas RJ, Allen GG (1997) Registration of head volume images using implantable fiducial markers. IEEE Trans Med Imag 16(4):447–462CrossRefGoogle Scholar
  11. 11.
    Fox PT, Perlmutter JS, Raichle ME (1985) A stereotactic method of anatomical localization of positron emission tomography. J Comput Assist Tomogr 9:141–153CrossRefGoogle Scholar
  12. 12.
    Evans AC, Marrett S, Collins L, Peters TM (1989) Anatomical-functional correlative analysis of the human brain using three dimensional imaging systems. Med Imag Process 1092:264–274Google Scholar
  13. 13.
    Strother SC, Anderson JR, Xu X, Liow J, Bonar DC, Rottenberg DA (1994) Quantitative comparisons of image registration techniques based on high-resolution MRI of the brain. J Comput Assist Tomogr 18(6):954–962CrossRefGoogle Scholar
  14. 14.
    Fitzpatrick JM, West JB, Maurer CR (1998) Predicting error in rigid-body point based registration. IEEE Trans Med Imag 17(5):694–702CrossRefGoogle Scholar
  15. 15.
    Grimpson WEL, Ettinger GJ, White SJ, Lozano-Perez T, Wells WM, Kikinis R (1996) An automatic registration method for frameless stereotaxy, image guided surgery, and enhanced reality visualization. IEEE Trans Med Imag 15(2):129–140CrossRefGoogle Scholar
  16. 16.
    Herring JL, Dawant BM, Maurer CR Jr, Muratore DM, Galloway RL, Fitzpatrick JM (1998) Surface-based registration of CT images to physical space for image-guided surgery of the spine: a sensitivity study. IEEE Trans Med Imag 17(5):743–752CrossRefGoogle Scholar
  17. 17.
    Kanatani K (1994) Analysis of 3-D rotation fitting. IEEE Trans Pattern Analysis Machine Intell 16(5):543–549Google Scholar
  18. 18.
    Declerc J, Feldmar J, Betting F, Goris ML (1997) Automatic registration and alignment on a template of cardiac stress and rest SPECT images. IEEE Trans Med Imag 16:727–737CrossRefGoogle Scholar
  19. 19.
    Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Recogn Mach Intell 14(2):239–256Google Scholar
  20. 20.
    Kim J, Fessler Jeffrey A (2004) Intensity-based image registration using robust correlation coefficients. IEEE Trans Med Imag 23(11):98–104CrossRefGoogle Scholar
  21. 21.
    Hazra J, Roy Chowdhury A, Dutta P (2013) An approach for determining angle of rotation of a gray image using weighted statistical regression. Int J Sci Eng Res 4(8):1006–1013Google Scholar
  22. 22.
    Hazra J, Roy Chowdhury A, Dutta P (2014) Statistical regression based rotation estimation technique of color image. Int J Comput Appl 102(15):1–4Google Scholar
  23. 23.
    Pluim JPW, Maintz JBA, Viergever MA (2003) Mutual-information based registration of medical images: a survey. IEEE Trans Med Imag 22(6):986–1004CrossRefGoogle Scholar
  24. 24.
    Langevin F, Didon JP (1995) Registration of MR images: from 2D to 3D, using a projection based cross correlation method. In: IEEE 17th Annual Conference, vol 1. Engineering in medicine and biology society, CanadaGoogle Scholar
  25. 25.
    Rangarajan A, Chui H, Duncan JS (1999) Rigid point feature registration using mutual information. Med Image Anal 3(4):425–440CrossRefGoogle Scholar
  26. 26.
    Puri BK (2004) Monomodal rigid-body registration and applications to the investigation of the effects of eicosapentaenoic acid intervention in neuropsychiatric disorders. Prostaglandins, Leukotrienes and Essential Fatty Acids (PLEFA) 3:137–200Google Scholar
  27. 27.
    AI-Azzawi N, Abdullah WAKW (2012) MRI monomodal feature based registration based on the efficiency of multiresolution representation and mutual information. American J Biomed Eng 2(3):98–104Google Scholar
  28. 28.
    Davis MH, Khotanzad A, Flaming DP (1996) 3D image matching using radial basis function neural network. In: WCNN’96: World congress on neural networks, pp 1174–1179Google Scholar
  29. 29.
    Sabisch T, Ferguson A, Bolouri H (1998) Automatic registration of complex images using a self organizing neural system. In: Proceedings of 1998 international joint conference on neural networks, pp 165–170Google Scholar
  30. 30.
    Elhanany I, Sheinfeld M, Beck A, Kadmon Y, Tal N, Tirosh D (2000) Robust image registration based on feedforward neural networks. In: Proceedings of 2000 IEEE international conference on systems, man and cybernetics, TN, USA, pp 1507–1511Google Scholar
  31. 31.
    Mostafa MG, Farag AA, Essock E (2000) Multimodality image registration and fusion using neural network. Information Fusion. FUSION 2000, vol 2, pp 10–13Google Scholar
  32. 32.
    Shang L, Cheng Lv J, Yi Z (2006) Rigid medical image registration using pca neural network. Neurocomputing 69(13–15):1717–1722Google Scholar
  33. 33.
    Goldberg DE (1989) Genetic algorithm in search, optimization and machine learning. Addison-Wesley Professional, ReadingGoogle Scholar
  34. 34.
    Castro L, Zuben F (1999) Artificial immune systems: Part I—Basic theory and applications. Technical Report TR—DCA 01/99Google Scholar
  35. 35.
    Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3(6):610–621Google Scholar
  36. 36.
    Renzetti FR, Zortea L (2011) Use of a gray level cooccurrence matrix to characterize duplex stainless steel phases microstructure. Frattura ed Integrita Strutturale 16:43–51Google Scholar
  37. 37.
    Tuan Anh Pham (2010) Optimization of texture feature extraction algorithm. Thesis Paper, The NetherlandsGoogle Scholar
  38. 38.
    Dasgupta D (1999) Artificial immune systems and their applications. Springer, BerlinGoogle Scholar
  39. 39.
    Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice-HallGoogle Scholar
  40. 40.
    Meva DT, Kumbharana CK, Kothari AD (2012) The study of adoption of neural network approach in fingerprint recognition. Int J Comput Appl 40(11):8–11Google Scholar
  41. 41.
  42. 42.
  43. 43.
    Goon AM, Gupta MK, Dasgupta B (2008) An outline of statistical theory. The World Press Private Limited, KolkataGoogle Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Joydev Hazra
    • 1
  • Aditi Roy Chowdhury
    • 2
  • Paramartha Dutta
    • 3
  1. 1.Department of Information TechnologyHeritage Institute of TechnologyKolkataIndia
  2. 2.Department of Computer Science and TechnologyBipradas Pal Chowdhury Institute of TechnologyKrishnagarIndia
  3. 3.Department of Computer and System SciencesVisva-Bharati UniversitySantiniketanIndia

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