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FWFusion: Fuzzy Whale Fusion model for MRI multimodal image fusion

  • Hanmant Venketrao Patil
  • Suresh D Shirbahadurkar
Article
  • 44 Downloads

Abstract

Medical treatment and diagnosis require information that is taken from several modalities of images like Magnetic Resonance Imaging (MRI), Computerized Tomography and so on. The information obtained for certain ailments is often incomplete, invisible and lacking in consistent scanner performance. Hence, to overcome these issues in the image modalities, image fusion schemes are developed in the literature. This paper proposes a hybrid algorithm using fuzzy concept and a novel P-Whale algorithm, called Fuzzy Whale Fusion (FWFusion), for the fusion of MRI multimodal images. Two multimodal images from MRI (T1, T1C, T2 and FLAIR) are considered as the source images, which are fed as inputs to a wavelet transform. The transform utilized converts the images into four different bands, which are fused using two newly derived fusion factors, fuzzy fusion and whale fusion, in a weighted function. The proposed P-Whale approach combines Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) for the effective selection of whale fusion factors. The performance of FWFusion model is compared to those of the existing strategies using Mutual Information (MI), Peak Signal-to-Noise Ratio (PSNR) and Root Mean Squared Error (RMSE), as the evaluation metrics. From the mean performance evaluation, it is observed that the proposed approach can achieve MI of 1.714, RMSE of 1.9 and PSNR of 27.9472.

Keywords

Image fusion optimization wavelet transform fuzzy fusion factor whale fusion factor 

References

  1. 1.
    Wang Z, Ziou D, Armenakis C, Li D and Li Q 2005 A comparative analysis of image fusion methods. IEEE Trans. GeoSci. Remote Sens. 43(5): 1391–1402CrossRefGoogle Scholar
  2. 2.
    Kim Y, Lee C, Han D, Kim Y and Kim Y 2011 Improved additive-wavelet image fusion. IEEE Trans. Geosci. Remote Sens. Lett. 8(2): 263–267CrossRefGoogle Scholar
  3. 3.
    Goshtas A A and Nikolov S 2007 Image fusion: advances in the state of the art. Inf. Fus. 8(2): 114–118CrossRefGoogle Scholar
  4. 4.
    Dammavalam S R, Maddala S and Krishna Prasad M H M 2012 Comparison of fuzzy and neuro-fuzzy image fusion techniques and its applications. Int. J. Comput. Appl. 43(19): 31–37Google Scholar
  5. 5.
    Dammavalam S R, Maddala S and Krishna Prasad M H M 2012 Quality assessment of pixel level image fusion using fuzzy logic. Int. J. Soft Comput. 3(1): 11–23CrossRefGoogle Scholar
  6. 6.
    Kavitha S and Thyagharajan K K 2017 Efficient DWT-based fusion techniques using genetic algorithm for optimal parameter estimation. J. Soft Comput. 21(12): 3307–3316CrossRefGoogle Scholar
  7. 7.
    James A P and Dasarathy B V 2014 Medical image fusion: a survey of the state-of-the-art. Inf. Fus. 19: 4–19CrossRefGoogle Scholar
  8. 8.
    Barra V and Boire J V 2001 A general framework for the fusion of anatomical and functional medical images. NeuroImage 13(3): 410–424CrossRefGoogle Scholar
  9. 9.
    Khalegi B, Khamis A, Karray F O, et al 2013 Multisensor data fusion: a review of the state-of-the-art. Inf. Fus. 14(1): 28–44CrossRefGoogle Scholar
  10. 10.
    Rajkumar S and Kavitha S 2010 Redundancy discrete wavelet transform and contourlet transform for multimodality medical image fusion with quantitative analysis. In: Proceedings of the 3rd IEEE International Conference on Emerging Trends in Engineering and Technology, pp. 134–139Google Scholar
  11. 11.
    Shah P, Merchant S N and Desai U B 2013 Multifocus and multispectral image fusion based on pixel significance using multiresolution decomposition. Signal Image Video Process. 7(1): 95–109CrossRefGoogle Scholar
  12. 12.
    Bhateja V, Patel H, Krishn A, Sahu A and 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
  13. 13.
    Yang Y, Park D S, Huang S and Rao N 2010 Medical image fusion via an effective wavelet-based approach. EURASIP J. Adv. Signal Process. 2010: 579341CrossRefGoogle Scholar
  14. 14.
    Bhavana V and Krishnappa H K 2015 Multi-modality medical image fusion using discrete wavelet transform. In: Proceedings of 4th international conference on eco-friendly computing and communication systems, vol. 70, pp. 625–631Google Scholar
  15. 15.
    Koley S, Galande A, Kelkar B, Sadhu A K, Sarkar D and Chakraborty C 2016 Multispectral MRI image fusion for enhanced visualization of meningioma brain tumors and edema using contourlet transform and fuzzy statistics. J. Med. Biol. Eng. 36(4): 470–484CrossRefGoogle Scholar
  16. 16.
    Srivastava R, Prakash O and Khare A 2016 Local energy-based multimodal medical image fusion in curvelet domain. IET Comput. Vis. 10(6): 513–527CrossRefGoogle Scholar
  17. 17.
    Vijayarajan R 2015 Discrete wavelet transform based principal component averaging fusion for medical images. Int. J. Electron. Commun. 69(6): 896–902CrossRefGoogle Scholar
  18. 18.
    Lu H, Zhang L and Serikawa S 2012 Maximum local energy: an effective approach for multisensor image fusion in beyond wavelet transform domain. Comput. Math. Appl. 64(5): 996–1003CrossRefMATHGoogle Scholar
  19. 19.
    Xu X, Wang Y and Chen S 2016 Medical image fusion using discrete fractional wavelet transform. Biomed. Signal Process. Control 27: 103–111CrossRefGoogle Scholar
  20. 20.
    De A, Kumar S K, Gunasekaran A and Tiwari M K 2017 Sustainable maritime inventory routing problem with time window constraints. Eng. Appl. Artif. Intell. 61: 77–95CrossRefGoogle Scholar
  21. 21.
    De A, Mamanduru V K R, Gunasekaran A, Subramanian N and Tiwari M K 2016 Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization. Comput. Ind. Eng. 96: 201–215CrossRefGoogle Scholar
  22. 22.
    Maiyar L M and Thakkar J J 2017 A combined tactical and operational deterministic food grain transportation model: particle swarm based optimization approach. Comput. Ind. Eng. 110: 30–42CrossRefGoogle Scholar
  23. 23.
    Irshad H, Kamran M, Siddiqui A B and Hussain A 2009 Image fusion using computational intelligence: a survey. In: Proceedings of the Second International Conference on Environmental and Computer Science, pp. 128–132Google Scholar
  24. 24.
    Gonzalez R C and Woods R E 2009 Digital image processing. India: Pearson EducationGoogle Scholar
  25. 25.
    Choi M 2006 A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter. IEEE Trans. Geosci. Remote Sens. 44(6): 1672–1682CrossRefGoogle Scholar
  26. 26.
    Nikolov S, Hill P, Bull D and Canagarajah N 2001 Wavelets for image fusion. In: Wavelets in signal and image analysis, vol. 19, pp. 213–241 (chapter)Google Scholar
  27. 27.
    Mirjalili S and Lewis A 2016 The whale optimization algorithm. Adv. Eng. Softw. 95: 51–67CrossRefGoogle Scholar
  28. 28.
    Chander S, Vijaya P and Dhyani P 2016 Fractional Lion Algorithm – an optimization algorithm for data clustering. J. Comput. Sci. 12(7): 323–340CrossRefGoogle Scholar
  29. 29.
    Chander S, Vijaya P and Dhyani P 2016 MKF-firefly: hybridization of firefly and multiple kernel-based fuzzy C-means algorithm. Int. J. Adv. Res. Comput. Commun. Eng. 5(7): 213–216CrossRefGoogle Scholar
  30. 30.
    Gong Y J, et al 2016 Genetic learning particle swarm optimization. IEEE Trans. Cybernet. 46(10): 2277–2290CrossRefGoogle Scholar
  31. 31.

Copyright information

© Indian Academy of Sciences 2018

Authors and Affiliations

  • Hanmant Venketrao Patil
    • 1
  • Suresh D Shirbahadurkar
    • 2
  1. 1.Department of Electronics and TelecommunicationNDMVPS’s KBT COENashikIndia
  2. 2.Department of Electronics and TelecommunicationZeal Education SocietyPuneIndia

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