Skip to main content
SpringerLink
Log in

Medical Image Fusion in Curvelet Domain Employing PCA and Maximum Selection Rule

  • Conference paper
  • First Online:
Proceedings of the Second International Conference on Computer and Communication Technologies

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 379))

Abstract

Curvelet transform achieves a compact representation of edges and curved shapes in the image, which other techniques like wavelets and ridgelets are not able to represent. This property of curvelet transform facilitates the retrieval of complementary information from medical images for precise and efficient clinical diagnosis. This paper presents a combination of curvelet transform along with principal component analysis (PCA) and maximum selection rule as an improved fusion approach for MRI and CT-scan. The proposed fusion approach involves image decomposition using curvelet transform followed by application of PCA for dimensionality reduction and the selection of maximum matrix to select only the relevant information in the images. Fusion factor (FF) and structural similarity index (SSIM) are used for performance evaluation of the proposed approach. Simulation results demonstrate an improvement in visual quality of the fused image supported by higher values of fusion metrics.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dasarathy, B.V.: information fusion in the realm of medical applications—a bibliographic glimpse at its growing appeal. Inf. Fus. 13(1), 1–9 (2012)

    Article  Google Scholar 

  2. Schoder H., Yeung H.W., Gonen M., Kraus D., Larson S.M.: Head and neck cancer: clinical usefulness and accuracy of PET/CT image fusion. Radiology 65–72 (2004)

    Google Scholar 

  3. Nakamoto Y., Tarnai K., Saga T., Higashi T., Hara T., Suga T., Koyama T., Togashi K.: Clinical value of image fusion from MR and PET in patients with head and neck cancer. Mol. Imaging Biol. 46–53 (2009)

    Google Scholar 

  4. Singh, R., Khare, A.: Fusion of multimodal images using daubechies complex wavelet transform—a multiresolution approach. Info. Fusion. 19, 49–60 (2014)

    Article  Google Scholar 

  5. Tu, T.M., Su, S.C., Shyu, H.C., Huang, P.S.: A new look at IHS-like image fusion methods. Inf. Fusion 2(3), 177–186 (2001)

    Article  Google Scholar 

  6. Gillespie, A.R., Kahle, A.B., Walker, R.E.: Color enhancement of highly correlated images—II. Channel ratio and ‘chromaticity’ transformation techniques. Remote Sens. Environ. 22, 343–365 (1987)

    Article  Google Scholar 

  7. Mallat S.: A theory for multi-resolution signal: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693, July (1989)

    Google Scholar 

  8. Shensa M.J.: The discrete wavelet transform: wedding the à Trous and Mallat algorithms. IEEE Trans. Signal Process. 40(10), 2464–2482, Oct (1992)

    Google Scholar 

  9. Sahu A., Bhateja V., Krishn A., Himanshi: Medical image fusion with Laplacian pyramids. In: Proceedings of (IEEE) International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MEDCOM-2014), Gr. Noida, 448–453, Nov (2014)

    Google Scholar 

  10. Krishn A., Bhateja V., Himanshi, Sahu A.: medical image fusion using combination of PCA and wavelet analysis. In: Proceedings of 3rd (IEEE) International Conference on Advances in Computing, Communications and Informatics (ICACCI-2014), pp. 986–991. Gr. Noida, India, Sept (2014)

    Google Scholar 

  11. Himanshi, Bhateja V., Krishn A. and Sahu A.: An improved medical image fusion approach using PCA and complex wavelets. In: Proceedings of (IEEE) International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MEDCOM-2014), pp. 442–447. Gr. Noida, India, Nov (2014)

    Google Scholar 

  12. Aboubaker M. ALEjaily: Fusion of Remote Sensing Images Using Contourlet Transform. Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering, Springer 213–218 (2008)

    Google Scholar 

  13. Cunha, L.D., Zhou, J.P.: The non-subsampled contourlet transform: theory, design, and applications. IEEE Tran. Image Proc. 15(10), 3089–3101 (2006)

    Article  Google Scholar 

  14. Luo, Y., Liu, R., Zhu, Y.Z.: Fusion of remote sensing image based on the PCA and atrous wavelet transform. Int. Archives Photogram. Remote Sens. Spat. Info. Sci. XXXVII(Part B7), 1155–1158 (2008)

    Google Scholar 

  15. Petrovic, V.S., Costas, S.X.: Gradient-based multiresolution image fusion. IEEE Trans. Image Process. 13(2), 228–237 (2004)

    Article  Google Scholar 

  16. Sadhasivam, S.K., Keerthivasan, M.K., Muttan, S.: Implementation of max principle with PCA in image fusion for surveillance and navigation application. Electron. Lett. Comput. Vision Image Anal. 10(1), 1–10 (2011)

    Google Scholar 

  17. Jain, A., Singh, S., Bhateja, V.: A robust approach for denoising and enhancement of mammographic breast masses. Int. J Converg. Comput. 1(1), 38–49 (2013). Inderscience Publishers

    Article  Google Scholar 

  18. Shrivastava, A., Alankrita, Raj, A., Bhateja, V.” Combination of wavelet transform and morphological filtering for enhancement of magnetic resonance images. In: Proceedings of International Conference on Digital Information Processing and Communications (ICDIPC 2011), CCIS-188, pp. 460–474, July 2011

    Google Scholar 

  19. Bhateja, V., Urooj, S., Pandey, A., Misra, M., Lay-Ekuakille, A.: A polynomial filtering model for enhancement of mammogram lesions. In: Proceedings of IEEE International Symposium on Medical Measurements and Applications, pp. 97–100 (2013)

    Google Scholar 

  20. Siddhartha, Gupta, R., Bhateja, V.: A log-ratio based unsharp masking (UM) approach for enhancement of digital mammograms. In: Proceedings of CUBE International Information Technical Conference and Exhibition, pp. 26–31 (2012)

    Google Scholar 

  21. Bhateja, V., Devi, S.: An improved non-linear transformation function for enhancement of mammographic Breast masses. In: Proceedings of 3rd International Conference on Electronics and Computer Technology, vol. 5, pp. 341–346 (2011)

    Google Scholar 

  22. Bhateja, V., Misra, M., Urooj, S., Lay-Ekuakille, A.: A robust polynomial filtering framework for mammographic image enhancement from biomedical sensors. IEEE Sens. J. 13(11), 4147–4156 (2013)

    Article  Google Scholar 

  23. Bhateja, V., Devi, S.: A novel framework for edge detection of microcalcifications using a non-linear enhancement operator and morphological filter. In: Proceedings of (IEEE) 3rd International Conference on Electronics and Computer Technology (ICECT-2011), Kanyakumari, vol. 5, pp. 419–424, April 2011

    Google Scholar 

  24. Raj, A., Alankrita, Shrivastava, A., Bhateja, V.: Computer aided detection of brain tumor in MR images. Int. J. Eng. Technol. (IACSIT-IJET) 3, 523–532 (Oct 2011)

    Google Scholar 

  25. Granai L., Moschetti F., Vandergheynst, P.: Ridgelet transform applied to motion compensated images. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 561–564, 6–10 April (2003)

    Google Scholar 

  26. Krishn A., Bhateja V., Himanshi, Sahu, A.: PCA based medical image fusion in ridgelet domain. In: Proceedings of 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA-2014), Advances in Intelligent Systems and Computing, vol. 328, pp. 475–482. Springer, Nov (2014)

    Google Scholar 

  27. Ali, F.E., El-Dokany, I.M., Saad, A.A., Abd El-Samie, F. E.: Curvelet fusion of MR and CT images. Prog. Electromagn. Res. 3, 215–224 (2008)

    Google Scholar 

  28. Stark J.L., Candes E.J. Donoho D.L.: The curvelet transform for denoising. IEEE Trans. Image Proc 11(6), 670–684, June (2002)

    Google Scholar 

  29. Candes E.J., Donoho D.L.: Curvelets—a surprisingly effective nonadaptive representation for objects with edges. In: Cohen, A., Rabut, C., Schumaker, L.L. (eds.) Saint-Malo Proceedings. Vanderbilt University Press, Nashville, pp. 1–10 (1999)

    Google Scholar 

  30. Candes, E.J., Donoho, D.L.: Ridgelets—a key to higher dimensional intermittency. Phil. Trans. R. Soc. Lond. A 357, 2495–2509 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  31. Naidu, V.P.S., Rao, J.R.: Pixel-level image fusion using wavelets and principal component analysis. Def. Sci. J. 58(3), 338–352 (2008)

    Article  Google Scholar 

  32. Piella G., Heijmans, H.: A new quality metric for image fusion. In: International Conference on Image Processing, Barcelona, 14 Sept (2003)

    Google Scholar 

  33. Bhateja, V., Srivastava, A., Kalsi, A.: Fast SSIM index for color images employing reduced-reference evaluation. In: Proceeidngs of International Conference on Frontiers in Intelligent Computing Theory and Applications, vol. 247, pp. 451–458, (2013)

    Google Scholar 

  34. Zheng, Y., Essock, E.A., Hansen, B.C.: An Advanced image fusion algorithm based on wavelet transform –incorporation with PCA and morphological processing. Proc. SPIE 5298, 177–187 (2004)

    Article  Google Scholar 

  35. Yang, B., Li, S.: Pixel-level image fusion with simultaneous orthogonal matching pursuit. Inf. Fusion. 13, 10–19 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, 227105, U.P, India

    Himanshi, Vikrant Bhateja, Abhinav Krishn & Akanksha Sahu

Authors
  1. Himanshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vikrant Bhateja
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abhinav Krishn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Akanksha Sahu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Himanshi .

Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, India

    Suresh Chandra Satapathy

  2. Department of CSE, CMR Technical Campus, Hyderabad, India

    K. Srujan Raju

  3. Computer Science & Engineering, Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  4. Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer India

About this paper

Cite this paper

Himanshi, Bhateja, V., Krishn, A., Sahu, A. (2016). Medical Image Fusion in Curvelet Domain Employing PCA and Maximum Selection Rule. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 379. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2517-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-2517-1_1

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2516-4

  • Online ISBN: 978-81-322-2517-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation