Skip to main content

Advertisement

Log in

On the use of UDWT and fuzzy sets for medical image fusion

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Multimodal medical image fusion is the process of obtaining relevant information from medical images. In order to create a single image that is better suitable for diagnosis, multiple images from various sources are combined in image fusion for medical images. Since many of the structures in medical images are rarely discernible and the merged image typically shows blurring and lags behind the accompanying data, the complexity of medical images is higher. The purpose of this research is to present a novel hybrid approach based on fuzzy sets and Undecimated Discrete Wavelet Transform (UDWT) for improved visual analysis and to lessen blurring of medical images. There is no decimation stage in UDWT. It is a non-orthogonal multiresolution decomposition. In medical images, fuzzy sets are essential for reducing ambiguity. This research paper UDWT after fuzzifying the source photos. The maximum selection criterion is used to combine low-frequency subbands in the UDWT domain, whereas the Modified Spatial Frequency (MSF) technique is utilized to combine high-frequency subbands. The inverse UDWT creates the merged image. Several pairs of photos are used to demonstrate multimodal medical image fusion's efficacy. The suggested algorithm has higher entropy (6.99 bits/pixel for MR-MRA), spatial frequency (SF) (27.95 cycles/millimeter for CT-MRI), edge-based image fusion measure (QAB/F) (0.94 for MRI-PET), and standard deviation (SD) (40.24 for X-ray-VA) as compared to other existing algorithms. The experimental data enlightens that the proposed (UDWT + Fuzzy set) approach outperforms other approaches discussed in the literature.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

Authors declare that all the data being used in the design and production cum layout of the manuscript is declared in the manuscript.

References

  1. Gopi Krishna E, Tirupal T (2015) Performance of image fusion techniques for satellite images. Int J Technol Res Eng 2(12):3184–3188

    Google Scholar 

  2. Shanker Mishra HO, Bhatnagar S (2014) MRI and CT image fusion based on wavelet transform. Int J Inf Comput Technol 4(1):47–52

    Google Scholar 

  3. Kaplan I, Kolupka E, Morrissey M (1998) MRI-ultrasound image fusion for 125I prostate implant treatment planning. Int J Radiat Oncol Biol Phys 42(1):294

    Google Scholar 

  4. Baum KG, Raerty K, Helguera M, Schmidt E (2007) Investigation of PET/MRI image fusion schemes for enhanced breast cancer diagnosis. Proceedings of IEEE seventh symposium conference on nuclear Science (NSS) 5:3774–3780

    Google Scholar 

  5. James AP, Dasarathy BV (2014) Medical image fusion: a survey of the state of the art. Inf Fusion 19(1):4–19

    Google Scholar 

  6. Azam MA, Khan KB, Salahuddin S et al (2022) A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics. Comput Biol Med 144:105253. https://doi.org/10.1016/j.compbiomed.2022.105253

    Article  Google Scholar 

  7. Dou W, Ruan S, Chen Y, Bloyet D, Constans JM (2007) A framework of fuzzy information fusion for the segmentation of brain tumour tissues on MR images. Image Vis Comput 25(2). https://doi.org/10.1016/j.imavis.2006.01.025

  8. Marshall S, Matsopoulos G, Brunt J (1995) Multiresolution morphological fusion of MR and CT images of the human brain. Proc IEEE Vis Image Signal Process 141:1–5

    Google Scholar 

  9. Yu Liu, Xun Chen, Rabab KW, Jane Wang Z (2019) Medical image fusion via convolutional sparsity based morphological component analysis. IEEE Signal Process Lett 26(3). https://doi.org/10.1109/LSP.2019.2895749

  10. Ali FE, El-Dokany IM, Saad AA et al (2008) Curvelet fusion of MR and CT images. Progress Electromagnet Res C 3:215–224

    Google Scholar 

  11. Lin KP, Yao WJ (1995) A SPECT-CT image fusion technique for diagnosis of head-neck cancer. IEEE Annu Conf Eng Med Biol Soc 1:377–378

    Google Scholar 

  12. Shangli C, Junmin H, Zhongwei L (2008) Medical image of PET/CT weighted fusion based on wavelet transform. IEEE Int Conf Bioinform Biomed Eng 2523–2525. https://doi.org/10.1109/ICBBE.2008.964

  13. Daneshvar S, Ghassemian H (2010) MRI and PET image fusion by combining IHS and retina-inspired models. Inf Fusion 11(2):114–123

    Google Scholar 

  14. Megalooikonomou V, Kontos D (2007) Medical data fusion for telemedicine. IEEE Eng Med Biol Mag 26(5):36–42

    Google Scholar 

  15. Barra V, Boire JY (2001) A general framework for the fusion of anatomical and functional medical images. Neuro Image 13(3):410–424

    Google Scholar 

  16. Holupka E, Kaplan I, Burdette E et al (1996) Ultrasound image fusion for external beam radiotherapy for prostate cancer. Int J Radiat Oncol Biol Phys 35(5):975–984

    Google Scholar 

  17. Hosseini HG, Alizad A, Fatemi M (2007). Integration of Vibro-Acoustography imaging modality with the traditional mammography. Int J Biomed Imaging. https://doi.org/10.1155/2007/40980

  18. Mitianoudis N, Stathaki T (2007) Pixel-based and region-based image fusion schemes using ICA bases. Inf Fusion 8(2):131–142

    Google Scholar 

  19. Naidu VPS, Raol JR (2008) Pixel-level image fusion using wavelets and principal component analysis. Def Sci J 58(3):338–352

    Google Scholar 

  20. Du J, Li W, Xiao B, Nawaz Q (2016) Union Laplacian pyramid with multiple features for medical image fusion. Neurocomputing 194:326–339

    Google Scholar 

  21. Bhatnagar G, Jonathan Wu QM, Liu Z (2015) A new contrast based multimodal medical image fusion framework. Neurocomputing 157:143–152

    Google Scholar 

  22. Toet A (1989) Image fusion by a ratio of low-pass pyramid. Pattern Recogn Lett 9(4):245–253

    Google Scholar 

  23. Toet A (1989) A morphological pyramidal image decomposition. Pattern Recogn Lett 9(4):255–261

    Google Scholar 

  24. He C, Liu Q, Li H, Wang H (2010) Multimodal medical image fusion based on HIS and PCA. Procedia Eng 7:280–285

    Google Scholar 

  25. Haddadpour M et al (2017) PET and MRI image fusion based on combination of 2-D Hilbert transform and IHS method. Biomed J. https://doi.org/10.1016/j.bj.2017.05.002

    Article  Google Scholar 

  26. Haghighat MBA, Aghagolzadeh A, Seyedarabi H (2011) Multi-focus image fusion for visual sensor networks in DCT domain. Comput Electr Eng 37(5):789–797

    Google Scholar 

  27. Shreyamsha Kumar BK (2013) Multifocus and multispectral image fusion based on pixel significance using discrete cosine harmonic wavelet transform. Signal Image Video Process 7:1125–1143. https://doi.org/10.1007/s11760-012-0361-x

  28. Naidu VPS, Divya M, Maha Lakshmi P (2017) Multi-modal medical image fusion using multi-resolution discrete sine transform. Control Data Fusion e-J 1(2):13–26

    Google Scholar 

  29. Yang Y, Park DS, Huang S et al (2010) Medical Image Fusion via an Effective Wavelet-Based Approach. EURASIP J Adv Signal Process 2010:579341. https://doi.org/10.1155/2010/579341

  30. Xua X, Wanga Y, Chen S (2016) Medical image fusion using discrete fractional wavelet transform. Biomed Signal Process Control 27:103–111

    Google Scholar 

  31. Chavan S, Pawar A, Talbar S (2016) Multimodality medical image fusion using rotated wavelet transform. In: Iyer B, Nalbalwar S, Pawade R (eds) ICCASP/ICMMD-2016, Advances in Intelligent Systems Research 137:627–635. https://doi.org/10.2991/iccasp-16.2017.89

  32. Shahdoosti HR, Mehrabi A (2018) Multimodal image fusion using sparse representation classification in tetrolet domain. Digit Signal Process. https://doi.org/10.1016/j.dsp.2018.04.002

  33. Das S, Chowdhury M, Kundu MK (2011) Medical image fusion based on ripplet transform type-I. Progress Electromagn Res B 30:355–370

    Google Scholar 

  34. Liu X, Mei W, Huiqian Du (2017) Structure tensor and nonsubsampled shearlet transform based algorithm for CT and MRI image fusion. Neurocomputing 235:131–139

    Google Scholar 

  35. Srilatha K, Kaviyarasu S (2015) An Efficient Directive Contrast Based Multi Modal Medical Image Fusion under Improved NSCT Domain. Res J Pharm Biol Chem Sci 6(5):775

    Google Scholar 

  36. Srivastava R, Prakash O, Khare A (2016) Local energy-based multimodal medical image fusion in curvelet domain. IET Comput Vision. https://doi.org/10.1049/iet-cvi.2015.0251

    Article  Google Scholar 

  37. Li Bo, Peng H, Wang J (2021) A novel fusion method based on dynamic threshold neural P systems and nonsubsampled contourlet transform for multi-modality medical images. Signal Process 178:1–13. https://doi.org/10.1016/j.sigpro.2020.107793

    Article  Google Scholar 

  38. Lia W, Xiea Y, Zhoua H, Hanb Y, Zhana K (2018) Structure-aware image fusion. Optik-Int J Light Electron Opt 172:1–11

    Google Scholar 

  39. Meher B, Agrawal S, Panda R, Abraham A (2018) A survey on region based image fusion methods. Inf Fusion. https://doi.org/10.1016/j.inffus.2018.07.010

    Article  Google Scholar 

  40. Prakash O, Park CM, Khare A, Jeon M, Gwak J (2019) Multiscale fusion of multimodal medical images using lifting scheme based biorthogonal wavelet transform. Optik. https://doi.org/10.1016/j.ijleo.2018.12.028

    Article  Google Scholar 

  41. Flower J (2005) The redundant discrete wavelet transform and additive noise. IEEE Signal Process Lett 12(9):629–632

    Google Scholar 

  42. Li X, He M, Roux M (2010) Multifocus image fusion based on redundant wavelet transform. IET Image Process 4(4):283–93

    Google Scholar 

  43. Zadeh LA (1965) Fuzzy sets. Inform Contr 8(3):338–353

    Google Scholar 

  44. Manchanda M, Sharma R (2016) A novel method of multimodal medical image fusion using fuzzy transform. J Vis Commun Image Represent 40:197–217

    Google Scholar 

  45. Toet A (1990) Hierarchical image fusion. Mach Vis Appl 3:1–11

    Google Scholar 

  46. Mikoajczyk K, Owczarczyk J, Recko W (1993) A test-bed for computer-assisted fusion of multi-modality medical images. In: Chetverikov D, Kropatsch WG (eds) Computer Analysis of Images and Patterns. CAIP 1993. Lecture Notes in Computer Science, vol 719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57233-3_89

  47. Mukhopadhyay S, Chanda B (2001) Fusion of 2d gray scale images using multiscale morphology. Pattern Recogn 2001(34):1939–1949

    Google Scholar 

  48. Bloch I, Colliot O, Camara O et al (2005) Fusion of spatial relationships for guiding recognition, example of brain structure recognition in 3D MRI. Pattern Recogn Lett 26(4):449–457

    Google Scholar 

  49. Li H, Deklerck R, Cuyper BD et al (1995) Object recognition in brain CT-scans: knowledge-based fusion of data from multiple feature extractors. IEEE Trans Med Imaging 14(2):212–229

    Google Scholar 

  50. Rogova GL, Stomper PC (2002) Information fusion approach to microcalcification characterization. Inf Fusion 3(2):91–102

    Google Scholar 

  51. Raza M, Gondal I, Green D et al (2001) Classifier fusion to predict breast cancer tumors based on microarray gene expression data. Knowledge-Based Intelligent Information and Engineering Systems. Springer, Berlin, pp 866–874

    Google Scholar 

  52. Qu G, Zhang D, Yan P (2001) Medical image fusion by wavelet transform modulus maxima. Opt Express 9(4):184–190

    Google Scholar 

  53. Kor S, Tiwary U (2004) Feature level fusion of multimodal medical images in lifting wavelet transform domain. In: Conf Proc IEEE Eng Med Biol Soc 2004:1479–1482. https://doi.org/10.1109/IEMBS.2004.1403455

  54. Yang L, Guo BL, Ni W (2008) Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform. Neuro Comput 72:203–211

    Google Scholar 

  55. Yang B, Jing Z (2008) Medical image fusion with a shift-invariant morphological wavelet. In: IEEE Conference on Cybernetics and Intelligent Systems, Chengdu, China, pp 175–178. https://doi.org/10.1109/ICCIS.2008.4670742

  56. Singh R, Vatsa M, Noore A (2009) Multimodal medical image fusion using redundant discrete wavelet transform. In: 2009 Seventh International Conference on Advances in Pattern Recognition, Kolkata, India, pp 232–235. https://doi.org/10.1109/ICAPR.2009.97

  57. Kavitha C, Chellamuthu C (2010) Multimodal medical image fusion based on integer wavelet transform and neuro-fuzzy. In: 2010 International Conference on Signal and Image Processing, Chennai, India, pp 296–300. https://doi.org/10.1109/ICSIP.2010.5697486

  58. Singh R, Khare A (2014) Fusion of multimodal medical images using Daubechies complex wavelet transform – a multiresolution approach. Inf Fusion 19:49–60

    Google Scholar 

  59. Yang Y, Tong S, Huang S et al (2014) Log-Gabor energy based multimodal medical image fusion in NSCT domain. Comput Math Methods Med. https://doi.org/10.1155/2014/835481

    Article  Google Scholar 

  60. Qiu C, Wang Y, Zhang H, Xia S (2017) Image fusion of CT and MR with sparse representation in NSST domain. Comput Math Methods Med. https://doi.org/10.1155/2017/9308745

    Article  MathSciNet  Google Scholar 

  61. Vakaimalar E, Mala K, Suresh Babu R (2019) Multifocus image fusion scheme based on discrete cosine transform and spatial frequency. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-7124-9

    Article  Google Scholar 

  62. Broussard RP, Rogers SK, Oxley ME et al (1999) Physiologically motivated image fusion for object detection using a pulse coupled neural network. IEEE Trans Neural Netw 10(3):554–563

    Google Scholar 

  63. Szu H, Kopriva I, Hoekstra P et al (2003) Early tumor detection by multiple infrared unsupervised neural nets fusion. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No. 03CH37439), Cancun, Mexico, pp 1133–1136 Vol. 2. https://doi.org/10.1109/IEMBS.2003.1279448

  64. Wang Z, Ma Y (2008) Medical image fusion using m-PCNN. Inf Fusion 9:176–185

    Google Scholar 

  65. Tang L, Qian J, Li L, Hu J, Wu X (2017) Multimodal medical image fusion based on discrete tchebichef moments and pulse coupled neural. Network 27:57–65

    Google Scholar 

  66. Na Y, Lu H, Zhang Y (2008) Content analysis based medical images fusion with fuzzy inference. In: 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, Jinan, China, pp 37–41. https://doi.org/10.1109/FSKD.2008.608

  67. Assareh A, Volkert LG (2009) Fuzzy rule base classifier fusion for protein mass spectra based ovarian cancer diagnosis. In: 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, Nashville, TN, USA, 2009, pp 193–199. https://doi.org/10.1109/CIBCB.2009.4925728

  68. Teng J, Wang S, Zhang J, et al (2010) Neuro-fuzzy logic based fusion algorithm of medical images. In: 2010 3rd International Congress on Image and Signal Processing, Yantai, China, pp 1552–1556. https://doi.org/10.1109/CISP.2010.5646958

  69. Das S, Kundu MK (2013) A neuro-fuzzy approach for medical image fusion. IEEE Trans Biomed Eng 60(12):3347–3353. https://doi.org/10.1109/TBME.2013.2282461

  70. Haribabu M, Guruviah V, Yogarajah P (2022) recent advancements in multimodal medical image fusion techniques for better diagnosis: an overview. Curr Med Imaging. https://doi.org/10.2174/1573405618666220606161137

    Article  Google Scholar 

  71. Muzammil SR, Maqsood S, Haider S, Damasevicius R (2020) CSID: a novel multimodal image fusion algorithm for enhanced clinical diagnosis. Diagnostics 10(11):904. https://doi.org/10.3390/diagnostics10110904

    Article  Google Scholar 

  72. Yadav SP, Yadav S (2020) Image fusion using hybrid methods in multimodality medical images. Med Biol Eng Comput 58(4):669–687. https://doi.org/10.1007/s11517-020-02136-6

    Article  Google Scholar 

  73. Eskicioglu A, Fisher P (1995) Image quality measures and their performance. IEEE Trans Commun 43(12):2959–2965

    Google Scholar 

  74. Li S, Yang B (2008) Multifocus image fusion using region segmentation and spatial frequency. Image Vis Comput 26(7):971–979

    Google Scholar 

  75. Xydeas CS, Petrovic V (2000) Objective image fusion performance measure. Electron Lett 36(4):308–309

    Google Scholar 

  76. www.med.harvard.edu. Accessed on 01–06–2023

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anand Nayyar.

Ethics declarations

Ethics approval

No Human subject or animals are involved in the research.

Consent to participate

All authors have mutually consented to participate.

Consent to publish

All the authors have consented the Journal to publish this paper.

Conflicts of interest

The authors declare that they have no conflicts of interest to report regarding the present study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tirupal, T., Pandurangaiah, Y., Roy, A. et al. On the use of UDWT and fuzzy sets for medical image fusion. Multimed Tools Appl 83, 39647–39675 (2024). https://doi.org/10.1007/s11042-023-16892-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16892-8

Keywords

Navigation