CFP last date
20 June 2024
Call for Paper
July Edition
IJCA solicits high quality original research papers for the upcoming July edition of the journal. The last date of research paper submission is 20 June 2024

Submit your paper
Know more
Reseach Article

An Efficient Medical Image Fusion Technique based on IT2FLDS

by Ramya H. R., B. K. Sujatha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 38
Year of Publication: 2019
Authors: Ramya H. R., B. K. Sujatha

Ramya H. R., B. K. Sujatha . An Efficient Medical Image Fusion Technique based on IT2FLDS. International Journal of Computer Applications. 181, 38 ( Jan 2019), 13-23. DOI=10.5120/ijca2019918377

@article{ 10.5120/ijca2019918377,
author = { Ramya H. R., B. K. Sujatha },
title = { An Efficient Medical Image Fusion Technique based on IT2FLDS },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2019 },
volume = { 181 },
number = { 38 },
month = { Jan },
year = { 2019 },
issn = { 0975-8887 },
pages = { 13-23 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2019918377 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T01:08:30.617417+05:30
%A Ramya H. R.
%A B. K. Sujatha
%T An Efficient Medical Image Fusion Technique based on IT2FLDS
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 38
%P 13-23
%D 2019
%I Foundation of Computer Science (FCS), NY, USA

Multiple modal medical image fusion is an essential method for medical imaging technologies. In these Multi-modal medical image fusion usually are Positron Emission Tomography(PET)) and Single-photon Emission Computer Tomography(SPECT), Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images are utilized. However, the conventional state-of-art-fusion-techniques consists of less redundant and less comprehensive information. Therefore, here, we present an image fusion technique to control non-linear uncertainties and provide stability based on IT2FLDS for multi-modal medical color images. The core idea is to perform fusion on color source images of either functional or structural type by extracting both large and small structural information which is rarely done in any other conventional state-of-art-techniques. This can be achieved with the help of IT2FLDS. The fuzzy membership functions based image fusion technique helps to combining low and high frequency components of multi-model medical color images. Experimental results proves the superiority of our proposed image fusion model in terms of visual and quantitative analysis like mutual information, standard deviation and Feature Point Similarity Measure.

  1. A. P. James and B. V. Dasarathy, "Medical image fusion: A survey of the state of the art," In! Fusion, vol. 19, pp. 4-19, 2014.
  2. C. 1. Price and K. 1. Friston, "Functional imaging studies of neuropsychological patients: applications and limitations," Neurocase, vol. 8, no. 5, pp. 345-354, 2002.
  3. Yu Liua , Xun Chena, Hu Penga, et al. Multi-focus image fusion with a deep convolutional neural network[J].Information Fusion, 2017,36:191–207.
  4. Lei Wang, Bin Li , Lian-fang Tian. Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shift-invariant shearlet coefficients[J]. Information Fusion,2014,19:20–28.
  5. M.A. Rahman, S. Liu, S. Lin, C. Wong, G. Jiang, N. Kwok, Image contrast en-hancement for brightness preservation based on dynamic stretching, Int. J. Image Proces. (IJIP) 9(4) (2015) 241.
  6. M.A. Rahman, S. Lin, C. Wong, G. Jiang, S. Liu, N. Kwok, Efficient colour image compression using fusion approach, Imaging Sci. J. (2015),
  7. A. Goshtasby, S. Nikolov, Image fusion: advances in the state of the art, Information Fusion 8 (2) (2007) 114–118.
  8. A. James, B. Dasarathy, Medical image fusion: A survey of the state of the art, Information Fusion 19 (2014) 4–19. 1, 2
  9. A. Wang, H. Sun, Y. Guan, The application of wavelet transform to multimodality medical image fusion, in: IEEE International Conference on Networking, Sensing and Control, 2006, pp. 270–274. 1, 3
  10. Q. Miao, C. Shi, P. Xu, M. Yang, Y. Shi, A novel algorithm of image fusion using shearlets, Optics Communications 284 (6) (2011) 1540–1547.
  11. A. James, S. Thiruvenkadam, J. Paul, M. Braun, Special issue on medical image computing and systems, Information Fusion 19 (2014) 2–3. 1, 2
  12. M.A. Musen, B. Middleton, R.A. Greenes, “Clinical decision-support systems”, Biomed. Inform. pp. 643–674, 2014.
  13. M. F. Akay, “Support vector machines combined with feature selection for breast cancer diagnosis”. Expert systems with applications, vol. 36, no. 2, pp. 3240-3247, 2009.
  14. M. Pota, M. Esposito, G. De Pietro, “Fuzzy partitioning for clinical DSSs using statistical information transformed into possibility-based knowledge”. Knowledge-Based Systems, vol. 67, pp. 1-15, 2014.
  15. L. Zadeh, "Fuzzy sets", Information Control, vol. 8, 1965, pp.338-353.
  16. T. Ishibuchi, H.; Yamamoto, “Rule weight specification in fuzzy rule-based classification systems,” IEEE Transactions on Fuzzy Systems, vol. 13, no. 4, pp. 428–435, 2005.
  17. J. Roubos, M. Setnes, and J. Abonyi, Learning fuzzy classification rules from data, 2000.
  18. J. M. Mendel: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Upper Saddle River, NJ Prentice-Hall, 2001.
  19. “Applications of type-2 fuzzy logic systems: Handling the uncertainty associated with surveys,” presented at FUZZ-IEEE Conf., Seoul, Korea, Aug. 1999.
  20. K. C. Wu, “Fuzzy interval control of mobile robots,” Comput. Elect. Eng., vol. 22, no. 3, pp. 211–229, 1996.
  21. A. Ross and A. K. Jain, "Multimodal biometrics: An overview," Signal Processing Conference, 2004 12th European, Vienna, 2004, pp. 1221-1224.
  22. J. Yang, Y. Wu, Y. Wang and Y. Xiong, "A novel fusion technique for CT and MRI medical image based on NSST," 2016 Chinese Control and Decision Conference (CCDC), Yinchuan, 2016, pp. 4367-4372.
  23. C. K. Chaitanya, G. S. Reddy, V. Bhavana and G. S. C. Varma, "PET and MRI medical image fusion using STDCT and STSVD," 2017 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, 2017, pp. 1-4.
  24. M. S. Dilmaghani, S. Daneshvar and M. Dousty, "A new MRI and PET image fusion algorithm based on BEMD and IHS methods," 2017 Iranian Conference on Electrical Engineering (ICEE), Tehran, 2017, pp. 118-121.
  25. Ye Liu, Yang Wang, Lisheng Wang and Shaoli Song, "Cerebral ASL, SPECT and MRI image registration, enhancing fusion and joint analysis system," 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Xi'an, 2016, pp. 70-74.
  26. V. Bhavana and H. K. Krishnappa, "Fusion of MRI and PET images using DWT and adaptive histogram equalization," 2016 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, 2016, pp. 0795-0798.
  27. S. Qi et al., "Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia," in IEEE Transactions on Medical Imaging, vol. 37, no. 1, pp. 93-105, Jan. 2018.
  28. Haddadpour M, Daneshavar S, Seyedarabi H. PET and MRI image fusion based on combination of Hilbert transform (2-D HT) and IHS method. Biomed J 2017.
  29. H. Liu et al., "Label fusion method based on sparse patch representation for the brain MRI image segmentation," in IET Image Processing, vol. 11, no. 7, pp. 502-511, 7 2017.
  30. J. R. Raol, and S.K. Kashyap, "Decision fusion using fuzzy logic type 1 in two aviation scenarios", Journal of Aerospace Sciences and Technologies, Vol. 65, No. 3, pp. 273-286, 2013.
  31. D. Wu and W. W. Tan, “A simplified architecture for type-2 FLSs and its application to nonlinear control,” in Proc. IEEE Conf. on Cybernetics and Intelligent Systems, Singapore, Dec. 2004, pp. 485–490.
  32. O. Castillo and P. Melin, “Recent Advances in Interval Type-2 Fuzzy Systems”, 2012, Springer
  33. J. M. Mendel and R. I. B. John, "Type-2 fuzzy sets made simple," in IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 117-127, Apr 2002.
  34. G. Bhatnagar, Q. M. J. Wu, and Z. Liu, “Directive contrast based mul-timodal medical image fusion in NSCT domain,” IEEE Trans. Multime-dia, vol. 15, no. 5, pp. 1014–1024, 2013.
  35. D. S, and K. MK, “A Neuro-Fuzzy Approach for Medical Image Fu-sion,” IEEE Trans. Biomed. Eng., vol. 60, no. 12, pp. 3347–3353, 2013.
Index Terms

Computer Science
Information Sciences


Multi-modal Magnetic Resonance Imaging Image fusion type-2 fuzzy logic