Skip to main content
SpringerLink
Log in

Multispectral MRI Image Fusion for Enhanced Visualization of Meningioma Brain Tumors and Edema Using Contourlet Transform and Fuzzy Statistics

  • Original Article
  • Published:
Journal of Medical and Biological Engineering Aims and scope Submit manuscript

Abstract

This paper proposes a multispectral magnetic resonance imaging (MRI) image fusion scheme for improved visualization of anatomical and pathological information of meningioma (MG) brain tumors that combines the contourlet transform and fuzzy statistics. The proposed fusion technique mainly targets the tumor and its surrounding hyperintense (edema) region, which leads to improved brain imaging informatics for radiologists. The developed methodology mainly consists of the contourlet transform for multiscale and directional decomposition, fuzzy entropy for fusing approximation coefficients, and region-based fuzzy energy for fusing detailed coefficients of two input images with the same orientations. Two fusion rules are established here in order to fuse corresponding lower- and higher-frequency subbands of images. The proposed methodology is applied to five various combinations (such as T1-weighted and T2-weighted, T1 post-contrast and T2-weighted etc.) generated from four modalities of MRI images (T1-weighted, T1 post-contrast, T2-weighted, and fluid-attenuated inversion recovery (FLAIR)). A total of 150 MRI images (30 images from each of five combinations) are considered from 20 cases of MG brain tumors. A quantitative evaluation of the proposed method is performed in terms of three performance measures. The performance is compared with that of existing medical image fusion techniques tested on the same dataset. Experimental results show the superiority of the proposed methodology in terms of both qualitative and quantitative measures, which also indicates that fused images contain enriched diagnostic information that can aid the detection of tumors and edema. A fusion of post-contrast T1-weighted MRI images with FLAIR and T2-weighted MRI images provided clinically relevant information.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Osborn, A. G. (1994). Diagnostic neuroradiology: A text/atlas. St.Loui: Mosby.

    Google Scholar 

  2. Zee, C. S., Kim, P., Go, J. L., & Conti, P. (2003). Imaging of intracranial and spinal neoplasms. In Z. Petrovich, L. W. Brady, M. L. Apuzzo, & M. Bamberg (Eds.), Combined modality therapy of central nervous system tumors (pp. 109–143). Berlin: Springer.

    Chapter  Google Scholar 

  3. Barra V., & Boire, J.-Y. (2000). Quantification of brain tissue volumes using MR/MR fusion. Conference Proceedings of The 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2, 1451–1454.

  4. Piella, G. (2003). A general framework for multiresolution image fusion: from pixels to regions. Information Fusion, 4(4), 259–280.

    Article  Google Scholar 

  5. Yang, B., Jing, Z., & Zhao, H. (2010). Review of pixel-level image fusion. Journal of Shanghai Jiaotong University (Science), 15(1), 6–12.

    Article  Google Scholar 

  6. Irshad, H., Kamran, M., Siddiqui, A. B., & Hussain, A. (2009). Image fusion using computational intelligence: A survey. Conference Proceedings of Second International Conference on Environmental and Computer Science, 128–132.

  7. Gonzalez, R. C., & Woods, R. E. (2009). Digital image processing. India: Pearson Education.

    Google Scholar 

  8. Calhoun, V. D., & Adali, T. (2009). Feature-based fusion of medical imaging data. IEEE Transactions on Information Technoogy in Biomedicine, 13(5), 711–720.

    Article  Google Scholar 

  9. Zheng, Y., Elmaghraby, A. S., & Frigui, H. (2006). Three-band MRI Image fusion utilizing the wavelet-based method optimized with two quantitative fusion metrics. Conference Proceedings of SPIE 6144, 61440R–61440R–12.

  10. Selesnick, I. W., Baraniuk, R. G., & Kingsbury, N. C. (2005). The dual-tree complex wavelet transform. IEEE Signal Processing Magazine, 22(6), 123–151.

    Article  Google Scholar 

  11. Yang, J., Wang, Y., Xu, W., & Dai, Q. (2008). Image coding using dual-tree discrete wavelet transform. IEEE Transactions on Image Processing, 17(9), 1555–1569.

    Article  MathSciNet  Google Scholar 

  12. Do, M. N., & Vetterli, M. (2005). The contourlet transform: an efficient directional multiresolution image representation. IEEE Transactions on Image Processing, 14(12), 2091–2106.

    Article  MathSciNet  Google Scholar 

  13. Zhu, W., Li, Q., Liu, S., Xu, K., & Li, T. (2010). Image fusion algorithm based on the second generation bandelet. Conference Proceedings of International Conference on E-Product E-Service and E-Entertainment, 1–3.

  14. Liu, F., Liu, J., & Gao, Y. (2007). Image fusion based on wedgelet and wavelet. Conference Proceedings of International Symposium on Intelligent Signal Processing and Communication Systems, 682–685.

  15. Shusterman, E., & Feder, M. (1994). Image compression via improved quadtree decomposition algorithms. IEEE Transactions on Image Processing, 3(2), 207–215.

    Article  Google Scholar 

  16. Chai, Y., He, Y., & Ying, C. (2008). CT and MRI image fusion based on contourlet using a novel rule. Conference Proceedings of The 2nd International Conference on Bioinformatics and Biomedical Engineering, 2064–2067.

  17. Al-Azzawi, N. A., Sakim, H. A. M., & Wan Abdullah, A. K. (2009). An efficient medical image fusion method using contourlet transform based on PCM. Conference Proceedings of IEEE Symposium on Industrial Electronics & Applications, 1, 11–14.

    Google Scholar 

  18. Al-Azzawi, N., Sakim, H. A. M., Wan Abdullah, A. K. & Ibrahim, H. (2009). Medical image fusion scheme using complex contourlet transform based on PCA. Conference Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5813–5816.

  19. Chen S., & Wu, Y. (2009). image fusion based on contourlet transform and fuzzy logic. Conference Proceedings of 2nd International Congress on Image and Signal Processing, 1–5.

  20. Ren, X., Zheng, Y., Hu, T. & Zhang, J. (2010). Image fusion based on NSCT and fuzzy logic. Conference Proceedings of International Conference on Multimedia Technoogy, 1–4.

  21. Teng, J., Wang, S., Zhang, J., & Wang, X. (2010). Fusion algorithm of medical images based on fuzzy logic. Conference Proceedings of Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2, 546–550.

  22. Li, S., Yin, H., & Fang, L. (2012). Group-sparse representation with dictionary learning for medical image denoising and fusion. IEEE Transactions on Biomedical Engineering, 59(12), 3450–3459.

    Article  Google Scholar 

  23. Li, T., & Wang, Y. (2012). Multiscaled combination of MR and SPECT images in neuroimaging: A simplex method based variable-weight fusion. Computer Methods and Programs in Biomedicine, 105(1), 31–39.

    Article  Google Scholar 

  24. Calvini, P., Massone, A. M., Nobili, F. M., & Rodriguez, G. (2006). Fusion of the MR image to SPECT with possible correction for partial volume effects. IEEE Transactions on Nuclear Science, 53(1), 189–197.

    Article  Google Scholar 

  25. Shen, R., Cheng, I., & Basu, A. (2013). Cross-scale coefficient selection for volumetric medical image fusion. IEEE Transactions on Biomedical Engineering, 60(4), 1069–1079.

    Article  Google Scholar 

  26. Pieper, S., Halle, M., & Kikinis, R. (2004). 3D SLICER. Conference Proceedings of IEEE International Symposium on Biomedical Imaging: Nano to Macro, 1, 632–635.

  27. Ma, J., & Plonka, G. (2010). The curvelet transform. IEEE Signal Processing Magazine, 27(2), 118–133.

    Article  Google Scholar 

  28. Burt, P., & Adelson, E. (1983). The Laplacian pyramid as a compact image code. IEEE Transactions on Communications, 31(4), 532–540.

    Article  Google Scholar 

  29. Bamberger, R., & Smith, M. (1992). A filter bank for the directional decomposition of images: theory and design. IEEE Transactions on Signal Processing, 40(4), 882–893.

    Article  Google Scholar 

  30. Chaira, T., & Ray, A. K. (2009). Fuzzy image processing and applications with MATLAB. Florida: CRC Press.

    MATH  Google Scholar 

  31. Gang, C. (2009). Discussion on New Integral Entropy and Energy of Fuzzy Sets. Conference Proceedings of Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 6, 181–185.

  32. Wang, W.-J., & Chiu, C.-H. (1999). Entropy and information energy for fuzzy sets. Fuzzy Sets and Systems, 108(3), 333–339.

    Article  MathSciNet  MATH  Google Scholar 

  33. Wang, Z., & Bovik, A. (2002). A universal image quality index. IEEE Signal Processing Letters, 9(3), 81–84.

    Article  Google Scholar 

  34. Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 623–656.

    Article  MathSciNet  MATH  Google Scholar 

  35. Seetha, M., MuraliKrishna, I. V., & Deekshatulu, B. L. (2005). Data fusion performance analysis based on conventional and wavelet transform techniques. Conference Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 4, 2842–2845.

  36. Altman, D. G., & Bland, J. M. (2011). How to obtain the P value from a confidence interval. BMJ, 343, d2304.

    Article  Google Scholar 

  37. Teng, J., Wang, S., Zhang, J., & Wang, X. (2010). Neuro-fuzzy logic based fusion algorithm of medical images. Conference Proceedings of 3rd International Congress on Image and Signal Processing, 4, 1552–1556.

  38. Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Applied statistics, 28, 100–108.

    Article  MATH  Google Scholar 

  39. Thada, V., & Jaglan, D. V. (2013). Comparison of Jaccard, Dice, Cosine similarity coefficient to find best fitness value for web retrieved documents using genetic algorithm. International Journal of Innovations in Engineering and Technology, 2(4), 202–205.

    Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the EKO CT & MRI Scan Centre at Medical College and Hospitals Campus, Kolkata, for providing brain MRI images. The corresponding author would like to acknowledge DAE-Young Scientist Research Award Scheme (2013/36/38-BRNS/2350, dt.25-11-2013) by the Board of Research in Nuclear Sciences (BRNS), Department of Atomic Energy, for financially supporting this research work.

Author information

Authors and Affiliations

  1. School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, 721302, India

    Subhranil Koley & Chandan Chakraborty

  2. Computer Engineering, Sardar Patel Institute of Technology, Mumbai, 400058, India

    Ashwini Galande

  3. MoebiuSutra Consulting Pvt. Ltd., Pune, 411038, India

    Bhooshan Kelkar

  4. EKO CT & MRI Scan Centre, Medical College and Hospitals Campus, Kolkata, 700073, India

    Anup K. Sadhu

  5. Variable Energy Cyclotron Center (VECC), Salt Lake, Kolkata, 700064, India

    Debranjan Sarkar

Authors
  1. Subhranil Koley
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ashwini Galande
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bhooshan Kelkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anup K. Sadhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Debranjan Sarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chandan Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chandan Chakraborty.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Koley, S., Galande, A., Kelkar, B. et al. Multispectral MRI Image Fusion for Enhanced Visualization of Meningioma Brain Tumors and Edema Using Contourlet Transform and Fuzzy Statistics. J. Med. Biol. Eng. 36, 470–484 (2016). https://doi.org/10.1007/s40846-016-0149-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40846-016-0149-5

Keywords

Search

Navigation