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Image compression approach with ridgelet transformation using modified neuro modeling for biomedical images

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Abstract

Image compression is applied to many fields such as television dissemination, remote sensing, image storage. Digitized images are compressed by a method which exploits the redundancy of the images so that the number of bits required to represent the image can be reduced with acceptable degradation of the decoded image. The humiliation of the image quality is limited with respect to the application used. There are various biomedical applications where accuracy is of major concern. To attain the objective of performance improvement with respect to decoded picture quality and compression ratios, in contrast to existing image compression techniques, an effective image coding technique which involves transforming the image into another domain with ridgelet function and then quantizing the coefficients with hybrid neural networks combining two different learning networks called auto-associative multilayer perceptron and self-organizing feature map is proposed. Ridge functions are effective in representing functions that have discontinuities along straight lines. Normal wavelet transforms not succeed to represent such functions effectively. The results obtained from the combination of finite ridgelet transform with hybrid neural networks found much better than that obtained from the JPEG2000 image compression system.

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References

  1. Wallace GK (1991) The JPEG still picture compression standard. Common ACM 34:30–44

    Article  Google Scholar 

  2. Bryan EU (2001) A tutorial on modern lossy wavelet image compression: foundations of JPEG 2000. IEEE Signal Process Mag 1053–5888

  3. DeVore RA, Jawerth B, Lucier BJ (1992) Image compression through wavelet transform coding. IEEE Trans Inf Theory 38:719–746

    Article  MathSciNet  MATH  Google Scholar 

  4. Mallat S (1990) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 37:2091–2110

    Google Scholar 

  5. Xiong Z, Ramchandran K, Orchad M (1997) Space-frequency quantization for wavelet image coding. IEEE Trans Signal Process 6:677–693

    Google Scholar 

  6. Adelson EH, Simoncelli E, Hingorani R (1987) Orthogonal pyramid transforms for image coding. Proc SPIE 845:50–58

    Article  Google Scholar 

  7. Shapiro J (1993) Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans Signal Process 41:3445–3462

    Article  MATH  Google Scholar 

  8. Mallat S, Falzon F (1998) Analysis of low bit rate image transform coding. IEEE Trans Signal Process 46(4):1027–1042

    Article  Google Scholar 

  9. Minh ND, Martin V (2003) The finite ridgelet transform for image representation. IEEE Trans Image Process 12(1)

  10. Candes EJ (1998) Ridgelets: theory and applications. Ph.D. thesis, Department of Statistics, Stanford University

  11. Candes EJ, Donoho DL (1999) Ridgelets: a key to higher-dimensional intermittency. Phil Trans R Soc Lond A 357(1760):2495–2509. doi:10.1098/rsta.1999.0444. http://www-stat.stanford.edu/~candes/papers/RoySoc.pdf

  12. Bolker ED (1987) The finite Radon transform. Integral geometry. In: Bryant RL et al (eds) Contemporary Mathematics, vol 63. American Mathematical Society, Providence, pp 27–49

  13. Matus F, Flusser J (1993) Image representation via a finite Radon transform. IEEE Trans Pattern Anal Mach Intell 15(10):996–1006

    Article  Google Scholar 

  14. Rajakumar R (2009) Ridget transform on square integrable Boehmians. Bull Korean Math Soc 46(5):835–844

    Article  MathSciNet  MATH  Google Scholar 

  15. Rajakumar R (2012) Extension of ridgelet transform to tempered Boehmians. Novi Sad J Math 42(2):19–32

    MathSciNet  Google Scholar 

  16. Xu X, Zhang D (2008) Ridgelet and BP neural network based face detection method. In: Proceedings of the seventh world congress on intelligent control and automation pp 9276–9279

  17. Amjadya N, Keyniaa F, Zareipourb H (2011) Short-term wind power forecasting using ridgelet neural network. Electric Power Syst Res 81:2099–2107

    Article  Google Scholar 

  18. Kong W, Liu J (2013) Technique for image fusion based on nonsubsampled shearlet transform and improved pulse coupled neural network. Opt Eng 52(1)

  19. Joshi MS, Manthalhkar RR, Joshi YV (2010) Color image compression using wavelet and ridgelet transform. In: Proceedings of the seventh international conference on information technology pp 1318–1321

  20. Xiao-shan L, Guo-lan FU (2007) Image compression based on ridgelet transform and SPIHT. J Jiangxi Normal Univ (Natural Sciences Edition)

  21. Shu Z, Liu G, Wang S, Deng C, Gan L, Zhan L (2009) A novel image compression algorithm using ridgelet transformation with modified EBCOT. Proc Second Int Symp Electron Commer Secur 2:100–104

    Article  Google Scholar 

  22. Jain AK, Jianchang M, Mohiuddin KM (2002) Artificial neural networks: a tutorial. Computer 29:31–44

    Article  Google Scholar 

  23. Jilani SAK, Sattar SA (2010) Image compression approach for medical processing using modified neuro modeling. Int J Eng Sci Technol 2(9):4555–4559

    Google Scholar 

  24. Abidi MA, Yasuki S, Crilly PB (1994) Image compression using hybrid neural networks combining the auto-associative multi-layer perceptron and self organizing feature map. IEEE Trans Consum Electron 40(4):796–811

    Article  Google Scholar 

  25. Khalid S (2000) Introduction to data compression. Morgan Kaufmann

  26. Basso A, Kunt M (2008) Autoassociative neural networks for image compression. Eur Trans Telecommun 3(6):593–598. doi:10.1002/ett.4460030612

    Article  Google Scholar 

  27. Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464–1480

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Systems, College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

    Abdul Khader Jilani Saudagar

  2. Royal Institute of Technology and Science, R. R. Dist., Chevella (M), AP, India

    Abdul Sattar Syed

Authors
  1. Abdul Khader Jilani Saudagar
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  2. Abdul Sattar Syed
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Correspondence to Abdul Khader Jilani Saudagar.

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Saudagar, A.K.J., Syed, A.S. Image compression approach with ridgelet transformation using modified neuro modeling for biomedical images. Neural Comput & Applic 24, 1725–1734 (2014). https://doi.org/10.1007/s00521-013-1414-y

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  • DOI: https://doi.org/10.1007/s00521-013-1414-y

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