Fast curvelet transform through genetic algorithm for multimodal medical image fusion

  • Muhammad Arif
  • Guojun WangEmail author
Methodologies and Application


Currently, medical imaging modalities produce different types of medical images to help doctors to diagnose illnesses or injuries. Each modality of images has its specific intensity. Many researchers in medical imaging have attempted to combine redundancy and related information from multiple types of medical images to produce fused medical images that can provide additional concentration and image diagnosis inspired by the information for the medical examination. We propose a new method and method of fusion for multimodal medical images based on the curvelet transform and the genetic algorithm (GA). The application of GA in our method can solve the suspicions and diffuse existing in the input image and can further optimize the characteristics of image fusion. The proposed method has been tested in many sets of medical images and is also compared to recent medical image fusion techniques. The results of our quantitative evaluation and visual analysis indicate that our proposed method produces the best advantage of medical fusion images over other methods, by maintaining perfect data information and color compliance at the base image.


Ridgelet transform Curvelet transform Genetic algorithm Mutual information Image fusion 



This work was supported in part by the National Natural Science Foundation of China under Grants 61632009, 61472451 in part by the Guangdong Provincial Natural Science Foundation under Grant 2017A030308006, and in part by the High-Level Talents Program of Higher Education in Guangdong Province under Grant 2016ZJ01.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Anderson CH (1988) Filter-subtract-decimate hierarchical pyramid signal analyzing and synthesizing technique. U.S. Patent No. 4,718,104Google Scholar
  2. Arif M et al (2018) SDN-based secure VANETs communication with fog computing. In: International conference on security, privacy and anonymity in computation, communication and storage. Springer, ChamGoogle Scholar
  3. Arif M, Wang G, Balas VE (2018) Secure VANETs: trusted communication scheme between vehicles and infrastructure based on fog computing. Stud Inform Control 27(2):235–246CrossRefGoogle Scholar
  4. Arif M, Abdullah NA, Phalianakote SK, Ramli N, Elahi M (2014) Maximizing Information of Multimodality Brain Image Fusion Using Curvelet Transform with Genetic Algorithm. In: Paper presented at the international conference on computer assisted system in health (CASH), 2014Google Scholar
  5. Arif M, Wang G, Chen S (2018a) Deep learning with non-parametric regression model for traffic flow prediction. In: 2018 IEEE 16th international conference on dependable, autonomic and secure computing, 16th international conference on pervasive intelligence and computing, 4th international conference on big data intelligence and computing and cyber science and technology congress (DASC/PiCom/DataCom/CyberSciTech). IEEEGoogle Scholar
  6. Arif M, Wang G, Peng T (2018b) Track me if you can? Query based dual location privacy in VANETs for V2V and V2I. In: 2018 17th IEEE international conference on trust, security and privacy in computing and communications/12th IEEE international conference on big data science and engineering (TrustCom/BigDataSE). IEEEGoogle Scholar
  7. Bhateja V, Patel H, Krishn A, Sahu A, Lay-Ekuakille A (2015) Multimodal medical image sensor fusion framework using cascade of wavelet and contourlet transform domains. IEEE Sens J 15(12):6783–6790CrossRefGoogle Scholar
  8. Bhuvaneswari C, Aruna P, Loganathan D (2014) A new fusion model for classification of the lung diseases using genetic algorithm. Egypt Inform J 15:69–77CrossRefGoogle Scholar
  9. Burt PJ, Kolczynski RJ (1993) Enhanced image capture through fusion. In: Paper presented at the proceedings of the fourth international conference on computer vision, 1993Google Scholar
  10. Burt PJ (1992) A gradient pyramid basis for pattern-selective image fusion. Proc Soc Inf Disp 1992:467–470Google Scholar
  11. Candès EJ, Donoho DL (2000) Curvelets: a surprisingly effective nonadaptice representation for objects with edges. In: Rabut C, Cohen A, Schumaker LL (eds) Curves and surfaces. Vanderbilt University Press, Nashville TNGoogle Scholar
  12. Candes E, Demanet L, Donoho D, Ying L (2006) Fast discrete curvelet transforms. Multiscale Model Simul 5(3):861–899MathSciNetzbMATHCrossRefGoogle Scholar
  13. Cands EJ, Donoho DL (1999) Ridgelets: A key to higher-dimensional intermittency? Philos Trans R Soc Lond Ser A Math Phys Eng Sci 357(1760):2495–2509MathSciNetzbMATHCrossRefGoogle Scholar
  14. Chai Y, Li H, Qu J (2010) Image fusion scheme using a novel dual-channel PCNN in lifting stationary wavelet domain. Opt Commun 283(19):3591–3602CrossRefGoogle Scholar
  15. Chang B, Fan W, Deng B (2013) Medical images fusion using parameterized logarithmic image processing model and wavelet sub-band selection schemes. In: Emerging technologies for information systems, computing, and management. Springer, pp 469–477Google Scholar
  16. Chen X, Kar S, Ralescu DA (2012) Cross-entropy measure of uncertain variables. Inf Sci 201:53–60MathSciNetzbMATHCrossRefGoogle Scholar
  17. Choi M, Kim RY, Kim M-G (2004) The curvelet transform for image fusion. Int Soc Photogramm Remote Sens ISPRS 2004(35):59–64Google Scholar
  18. Da Cunha AL, Zhou J, Do MN (2006) The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans Image Process 15(10):3089–3101CrossRefGoogle Scholar
  19. Dai Y, Wang G (2018) Analyzing tongue images using a conceptual alignment deep autoencoder. IEEE Access 6:5962–5972CrossRefGoogle Scholar
  20. Dai Y, Wang G, Li K-C (2018) Conceptual alignment deep neural networks. J Intell Fuzzy Syst 34(3):1631–1642CrossRefGoogle Scholar
  21. Daneshvar S, Ghassemian H (2010) MRI and PET image fusion by combining IHS and retina-inspired models. Inf Fusion 11(2):114–123CrossRefGoogle Scholar
  22. Deans S (1983) The radon transform and some of its applications. Krieger Publishing Company, FloridazbMATHGoogle Scholar
  23. Donoho DL, Duncan MR (2000) Digital curvelet transform: strategy, implementation, and experiments. In: Paper presented at the AeroSense 2000Google Scholar
  24. Du P, Liu S, Xia J, Zhao Y (2013) Information fusion techniques for change detection from multi-temporal remote sensing images. Inf Fusion 14(1):19–27CrossRefGoogle Scholar
  25. Escalante-Ramrez B (2008) The Hermite transform as an efficient model for local image analysis: an application to medical image fusion. Comput Electr Eng 34(2):99–110zbMATHCrossRefGoogle Scholar
  26. Gang L, Lei X, Xuequan C (2005) Overview of the applications of curvelet transform in image processing. J Comput Res Dev 8:1331–1337Google Scholar
  27. Garcia F, Mirbach B, Ottersten B, Grandidier F, Cuesta A (2010) Pixel weighted average strategy for depth sensor data fusion. In: Paper presented at the 17th IEEE International Conference on Image Processing (ICIP), 2010Google Scholar
  28. Guanqun T, Dapeng L, Guanghua L (2004) Application of wavelet analysis in medical image fusion. J Xidian Univ 31(1):82–86Google Scholar
  29. He C, Liu Q, Li H, Wang H (2010) Multimodal medical image fusion based on IHS and PCA. Procedia Eng 7:280–285CrossRefGoogle Scholar
  30. Henson RN, Flandin G, Friston KJ, Mattout J (2010) A parametric empirical Bayesian framework for fMRI-constrained MEG/EEG source reconstruction. Hum Brain Mapp 31(10):1512–1531CrossRefGoogle Scholar
  31. Himanshi BV, Krishn A, Sahu A (2015) Medical image fusion in curvelet domain employing PCA and maximum selection rule. In: Paper presented at the proceedings of the (Springer) 2nd international conference on computers and communication technologies (IC3T-2015), Hyderabad, IndiaGoogle Scholar
  32. Javaid Q et al (2016a) Efficient facial expression detection by using the Adaptive-Neuro-Fuzzy-Inference-System and the Bezier curve. Sindh Univ Res J-SURJ (Sci Ser) 48(3):595–600Google Scholar
  33. Javaid Q, Arif M, Talpur S (2016b) Segmentation and classification of calcification and hemorrhage in the brain using fuzzy C-mean and adaptive neuro-fuzzy inference system, Quaid-e-Awam Univ Res J Eng Sci Technol 15(1):50–63Google Scholar
  34. Javaid Q et al (2018) A hybrid technique for De-Noising multi-modality medical images by employing cuckoo’s search with curvelet transform. Mehran Univ Res J Eng Technol 37(1):29CrossRefGoogle Scholar
  35. Kotwal K, Chaudhuri S (2013) A novel approach to quantitative evaluation of hyperspectral image fusion techniques. Inf Fusion 14(1):5–18CrossRefGoogle Scholar
  36. Kumar S, Sharma YK (2011) Curvelet based multi-focus medical image fusion technique: comparative study with wavelet based approachGoogle Scholar
  37. Li T, Wang Y (2012) Multiscaled combination of MR and SPECT images in neuroimaging: a simplex method based variable-weight fusion. Comput Methods Programs Biomed 105(1):31–39CrossRefGoogle Scholar
  38. Li H, Manjunath B, Mitra SK (1995) Multisensor image fusion using the wavelet transform. Graph Models Image Process 57(3):235–245CrossRefGoogle Scholar
  39. Li S, Kwok JT, Wang Y (2002) Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images. Inf Fusion 3(1):17–23CrossRefGoogle Scholar
  40. Li X, He M, Roux M (2010) Multifocus image fusion based on redundant wavelet transform. IET Image Process 4(4):283–293CrossRefGoogle Scholar
  41. Li S, Yang B, Hu J (2011) Performance comparison of different multi-resolution transforms for image fusion. Inf Fusion 12(2):74–84CrossRefGoogle Scholar
  42. Lia W, Zhangb Q (2008) Study on data fusion methods with optimal information preservation between spectral and spatial based on high resolution imagery. Int Arch Photogramm Remote Sens Spatial Inf Sci 36(1):1227–1232Google Scholar
  43. Liu Z, Yin H, Chai Y, Yang SX (2014) A novel approach for multimodal medical image fusion. Expert Syst Appl 41:7425–7435CrossRefGoogle Scholar
  44. Mahyari AG, Yazdi M (2009) A novel image fusion method using curvelet transform based on linear dependency test. In: Paper presented at the international conference on digital image processing, 2009Google Scholar
  45. Mittal A, Soundararajan R, Bovik AC (2013) Making a completely blind image quality analyzer. IEEE Signal Process Lett 20(3):209–212CrossRefGoogle Scholar
  46. Muhammad A, Guojun W (2017) Segmentation of calcification and brain hemorrhage with midline detection.” 2017 IEEE international symposium on parallel and distributed processing with applications and 2017 IEEE international conference on ubiquitous computing and communications (ISPA/IUCC). IEEEGoogle Scholar
  47. Nencini F, Garzelli A, Baronti S, Alparone L (2007) Remote sensing image fusion using the curvelet transform. Inf Fusion 8(2):143–156CrossRefGoogle Scholar
  48. Park PC, Schreibmann E, Roper J, Elder E, Crocker I, Fox T, Zhu XR, Dong L, Dhabaan A (2015) MRI-based computed tomography metal artifact correction method for improving proton range calculation accuracy. Int J Radiat Oncol* Biol* Phys 91(4):849–856CrossRefGoogle Scholar
  49. Peng Z, Wang G (2017) A novel ECG eigenvalue detection algorithm based on wavelet transform. BioMed Res Int 2017:1–12Google Scholar
  50. Rockinger O (1997) Image sequence fusion using a shift-invariant wavelet transform. In: Paper presented at the proceedings of the international conference on image processing, 1997Google Scholar
  51. Satpathy A, Jiang X, Eng H-L (2010) Extended histogram of gradients feature for human detection. In: Paper presented at the 17th IEEE international conference on image processing (ICIP), 2010Google Scholar
  52. Singh S, Gupta D, Anand R, Kumar V (2015) Nonsubsampled shearlet based CT and MR medical image fusion using biologically inspired spiking neural network. Biomed Signal Process Control 18:91–101CrossRefGoogle Scholar
  53. Singh R, Vatsa M, Noore A (2009) Multimodal medical image fusion using redundant discrete wavelet transform. In: Paper presented at the seventh international conference on advances in pattern recognition, 2009. ICAPR’09Google Scholar
  54. Sisniega A, Zbijewski W, Xu J, Dang H, Stayman J, Yorkston J, Siewerdsen J (2015) High-fidelity artifact correction for cone-beam CT imaging of the brain. Phys Med Biol 60(4):1415CrossRefGoogle Scholar
  55. Starck J-L, Cands EJ, Donoho DL (2002) The curvelet transform for image denoising. IEEE Trans Image Process 11(6):670–684MathSciNetzbMATHCrossRefGoogle Scholar
  56. Sui J, Pearlson G, Caprihan A, Adali T, Kiehl KA, Liu J, Calhoun VD (2011) Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model. Neuroimage 57(3):839–855CrossRefGoogle Scholar
  57. Tang J, Rahmim A (2009) Bayesian PET image reconstruction incorporating anato-functional joint entropy. Phys Med Biol 54(23):7063CrossRefGoogle Scholar
  58. Tessens L, Ledda A, Pizurica A, Philips W (2007) Extending the depth of field in microscopy through curvelet-based frequency-adaptive image fusion. Paper presented at the IEEE international conference on acoustics, speech and signal processing, 2007. ICASSP 2007Google Scholar
  59. Toet A (1989) A morphological pyramidal image decomposition. Pattern Recognit Lett 9(4):255–261zbMATHCrossRefGoogle Scholar
  60. Toet A (1989) Image fusion by a ratio of low-pass pyramid. Pattern Recognit Lett 9(4):245–253zbMATHCrossRefGoogle Scholar
  61. Toet A, Van Ruyven LJ, Valeton JM (1989) Merging thermal and visual images by a contrast pyramid. Opt Eng 28(7):287789–287789CrossRefGoogle Scholar
  62. Wan T, Canagarajah N, Achim A (2009) Segmentation-driven image fusion based on alpha-stable modeling of wavelet coefficients. IEEE Trans Multimed 11(4):624–633CrossRefGoogle Scholar
  63. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612CrossRefGoogle Scholar
  64. Wang Z, Ma Y, Gu J (2010) Multi-focus image fusion using PCNN. Pattern Recognit 43(6):2003–2016zbMATHCrossRefGoogle Scholar
  65. Wang L, Li B, Tian L-F (2014) Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shift-invariant shearlet coefficients. Inf Fusion 19:20–28CrossRefGoogle Scholar
  66. Wang L, Li B, Tian L (2014) Multi-modal medical volumetric data fusion using 3D discrete shearlet transform and global-to-local rule. IEEE Trans Biomed Eng 61(1):197–206CrossRefGoogle Scholar
  67. Xu Z (2014) Medical image fusion using multi-level local extrema. Inf Fusion 19:38–48CrossRefGoogle Scholar
  68. Yang L, Guo B, Ni W (2008) Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform. Neurocomputing 72(1):203–211CrossRefGoogle Scholar
  69. Yang S, Wang M, Jiao L, Wu R, Wang Z (2010) Image fusion based on a new contourlet packet. Inf Fusion 11(2):78–84CrossRefGoogle Scholar
  70. Yang J, Han F, Zhao D (2011) A block advanced PCA fusion algorithm based on PET/CT. In: Paper presented at the international conference on Intelligent Computation Technology and Automation (ICICTA), 2011Google Scholar
  71. Yi S, Labate D, Easley GR, Krim H (2009) A shearlet approach to edge analysis and detection. IEEE Trans Image Process 18(5):929–941MathSciNetzbMATHCrossRefGoogle Scholar
  72. Yuan Q, Dong C, Wang Q (2009) An adaptive fusion algorithm based on ANFIS for radar/infrared system. Expert Syst Appl 36(1):111–120CrossRefGoogle Scholar
  73. Zhang Q, Guo B (2009) Multifocus image fusion using the nonsubsampled contourlet transform. Signal Process 89(7):1334–1346zbMATHCrossRefGoogle Scholar
  74. Zhang Z, Sun S, Zheng F (2001) Image fusion based on median filters and SOFM neural networks: a three-step scheme. Signal Process 81(6):1325–1330zbMATHCrossRefGoogle Scholar
  75. Zhao Y, Zhao Q, Hao A (2014) Multimodal medical image fusion using improved multi-channel PCNN. Bio-med Mater Eng 24(1):221–228MathSciNetGoogle Scholar
  76. Ziran PENG et al (2017) Research and improvement of ECG compression algorithm based on EZW. Comput Methods Programs Biomed 145:157–166CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and Cyber EngineeringGuangzhou UniversityGuangzhouChina

Personalised recommendations