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Entropy improvement by the Temporal-Window method for Alternating and Non-Alternating 3D wavelet transform over angiographies

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Abstract

The three-dimensional wavelet transform (3D-WT) has been proposed for volumetric data coding, since it can provide lossless coding and top-quality reconstruction: two key features highly relevant to medical imaging applications. In this paper, we present experimental results for four new algorithms based on the Classic 3D-WT. The proposed algorithms are capable of obtaining the wavelet coefficients after the spatial and, mainly, the temporal decomposition processes, reducing most redundancies in the video sequence and getting lower entropy values than the Classic algorithm. The new algorithms are based on the Temporal-Window method for carrying out the temporal decomposition. We have conducted a set of experimental evaluations for a representative data set of a modality of intrinsically volumetric medical imaging: angiography sequences.

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References

  1. Antonini M, Barlaud M, Mathieu P, Daubechies I (1992) Image coding using wavelet transform. IEEE Trans Image Process 1, pp 205–220

    Google Scholar 

  2. Fournier A (1995) Wavelets and their application in computer graphics, Siggraph’95 course notes

  3. González RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, Englewood Cliffs

  4. ITU-T Recommendation H.262/ISO/IEC (1995) Information technology—generic coding of moving pictures and associated audio information

  5. Komatsu K, Sezaki K (1999) Lossless filter banks based on two point transform and interpolative prediction. In: Proceedings of IEEE international conference on acoustics, speech, and signal processing, vol 3, pp 1469–1472

  6. Mallat SG (1998) A wavelet tour of signal processing. Academic, San Diego

    MATH  Google Scholar 

  7. Mallat SG (1989) Multifrequency channel decompositions of images and wavelet models. IEEE Trans Acoust 37(12):2091–2110

    Article  Google Scholar 

  8. Moyano E, Quiles FJ, Orozco-Barbosa L (2004) Efficient algorithms for medical sequences decorrelation by 3D-Wavelet Transform. In: Proceedings 12th international conference on software, telecommunications and computer networks (SoftCOM), Croatia

  9. Moyano E, Quiles FJ, Garrido A, Orozco-Barbosa L, Duato J (2001) Efficient 3-D Wavelet Transform decomposition for video compression. In: 2nd International workshop on digital and computational video, USA

  10. Nielsen OM, Hegland M (2000) Parallel performance of fast wavelet transform. International Journal of High Speed Computing, World Scientific Publishing Company, Singapore 11-1

  11. Pearlman WA, Kim BJ, Xiong Z (1998) Embedded video subband coding with 3D SPIHT. In: Topiwala PN (ed) Wavelet image and video compression. Kluwer Academic, Dordrecht

  12. Said A, Pearlman WA (1996) An image multiresolution representation for lossless and lossy compression. IEEE Trans Image Process 5(9):1303–1310

    Article  Google Scholar 

  13. Silicon Graphics Inc, SGI Origin 3000, http://www.sgi.com/origin/3000/

  14. WG 11, MPEG I (1991) Coded Representation of picture, audio and multimedia/hypermedia information, ISO/IECJTC1/SC29

  15. Wu X, Qiu T (2005) Wavelet coding of volumetric medical images for high throughput and operability. IEEE Trans Med Imaging 24(6):719–727

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Information Technologies and Systems, University of Castilla-La Mancha, Cobertizo de San Pedro Mártir s/n, 45071, Toledo, Spain

    Encarnación Moyano-Ávila

  2. Computing Systems Department, University of Castilla-La Mancha, Escuela Politécnica Superior, 02071, Albacete, Spain

    Luis Orozco-Barbosa & Francisco J. Quiles

Authors
  1. Encarnación Moyano-Ávila
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  2. Luis Orozco-Barbosa
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  3. Francisco J. Quiles
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Corresponding author

Correspondence to Encarnación Moyano-Ávila.

Additional information

This work has been jointly supported by the Spanish MEC and European Comission FEDER funds under grants “Consolider Ingenio-2010 CSD2006-00046" and “TIN2006-15516-C04-02".

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Moyano-Ávila, E., Orozco-Barbosa, L. & Quiles, F.J. Entropy improvement by the Temporal-Window method for Alternating and Non-Alternating 3D wavelet transform over angiographies. Med Bio Eng Comput 45, 1121–1125 (2007). https://doi.org/10.1007/s11517-007-0254-2

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  • DOI: https://doi.org/10.1007/s11517-007-0254-2

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