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Home»Articles»Scalable Compression of 3-D Medical Image Data Using EBCOT with Volume of Interest Coding

JournalCover

Asian Journal of Computer Science and Technology (AJCST)

Editor Dr. K. Ganesh
Print ISSN : 2249-0701
Frequency : Quarterly

Scalable Compression of 3-D Medical Image Data Using EBCOT with Volume of Interest Coding

Author : Manimekalai and Anusuya
Volume 1 No.1 January-June 2012 pp 84-90

Abstract

Volumetric medical image data usually require a vast amount of resources for storage and transmission. With the wide pervasiveness of medical imaging applications in healthcare settings and the increased interest in telemedicine technologies, it has become essential to reduce both storage and transmission bandwidth requirements needed for archival and communication of related data, preferably by employing lossless compression methods to avoid any negative effects of lossy compression on image quality and diagnostic capabilities. The main objective is to present a 3-D medical image compression method with scalability properties, by quality and resolution up to lossless reconstruction and optimized Volume of Interest (VOI) coding at any bit-rate. The proposed method named as “EBCOT with VOI coding” employs a 3-D integer wavelet transform (3D-IWT) and a modified EBCOT with 3-D contexts to compress the 3-D medical imaging data into a layered bit-stream that is scalable by quality and resolution. VOI coding capabilities are attained after compression by employing a bit-stream reordering procedure, which is based on a weight assignment model that incorporates the position of the VOI and the mean energy of the wavelet coefficients. Optimized VOI coding at any bit-rate is attained by an optimization technique that maximizes the reconstruction quality of the VOI, while allowing for the decoding of background information with peripherally increasing quality around the VOI. Performance evaluations based on real 3-D medical imaging data showed that the proposed method achieves a higher reconstruction quality, in terms of the peak signal-to-noise ratio, than that achieved by 3D-JPEG2000 with VOI coding, when using the MAXSHIFT and general scaling-based methods.

Keywords

Embedded block coding with optimized truncation (EBCOT), Integer wavelet transform, medical image compression, scalable compression, volume of interest coding, 3D-JPEG2000

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Volumetric medical image data usually require a vast amount of resources for storage and transmission. With the wide pervasiveness of medical imaging applications in healthcare settings and the increased interest in telemedicine technologies, it has become essential to reduce both storage and transmission bandwidth requirements needed for archival and communication of related data, preferably by employing lossless compression methods to avoid any negative effects of lossy compression on image quality and diagnostic capabilities. The main objective is to present a 3-D medical image compression method with scalability properties, by quality and resolution up to lossless reconstruction and optimized Volume of Interest (VOI) coding at any bit-rate. The proposed method named as “EBCOT with VOI coding” employs a 3-D integer wavelet transform (3D-IWT) and a modified EBCOT with 3-D contexts to compress the 3-D medical imaging data into a layered bit-stream that is scalable by quality and resolution. VOI coding capabilities are attained after compression by employing a bit-stream reordering procedure, which is based on a weight assignment model that incorporates the position of the VOI and the mean energy of the wavelet coefficients. Optimized VOI coding at any bit-rate is attained by an optimization technique that maximizes the reconstruction quality of the VOI, while allowing for the decoding of background information with peripherally increasing quality around the VOI. Performance evaluations based on real 3-D medical imaging data showed that the proposed method achieves a higher reconstruction quality, in terms of the peak signal-to-noise ratio, than that achieved by 3D-JPEG2000 with VOI coding, when using the MAXSHIFT and general scaling-based methods.

Editor-in-Chief
Dr. K. Ganesh
Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
Expert at McKinsey and Company, India
[email protected]
Editorial Advisory Board
Dr. Eng. Hamid Ali Abed AL-Asadi
Department of Computer Science, Basra University, Iraq
[email protected]
Dr. Norjihan Binti Abdul Ghani
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Christos Bouras
Department of Computer Engineering & Informatics, University of Patras, Greece
[email protected]
Dr. Maizatul Akmar Binti Ismail
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Harold Castro
Department of Systems Engineering and Computing, University of the Andes, Colombia
[email protected]
Dr. Busyairah Binti Syd Ali
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. Sri Devi Ravana
Department of Information system, University of Malaya, Malaysia
[email protected]
Dr. Karpaga Selvi Subramanian
Department of Computer Engineering, Mekelle University, Ethiopia
[email protected]
Dr. Mazliza Binti Othman
Department of Computer System & Technology, University of Malaya, Malaysia
[email protected]
Dr. Chiam Yin Kia
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. OUH Eng Lieh
Department of Information Systems, Singapore Management University, Singapore
[email protected]

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    Editorial Note

    Editorial Dr. K. Ganesh

    Editor-in-Chief
    Dr. K. Ganesh
    Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
    Expert at McKinsey and Company, India
    [email protected]
    Editorial Advisory Board
    Dr. Eng. Hamid Ali Abed AL-Asadi
    Department of Computer Science, Basra University, Iraq
    [email protected]
    Dr. Norjihan Binti Abdul Ghani
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Christos Bouras
    Department of Computer Engineering & Informatics, University of Patras, Greece
    [email protected]
    Dr. Maizatul Akmar Binti Ismail
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Harold Castro
    Department of Systems Engineering and Computing, University of the Andes, Colombia
    [email protected]
    Dr. Busyairah Binti Syd Ali
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Sri Devi Ravana
    Department of Information system, University of Malaya, Malaysia
    [email protected]
    Dr. Karpaga Selvi Subramanian
    Department of Computer Engineering, Mekelle University, Ethiopia
    [email protected]
    Dr. Mazliza Binti Othman
    Department of Computer System & Technology, University of Malaya, Malaysia
    [email protected]
    Dr. Chiam Yin Kia
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. OUH Eng Lieh
    Department of Information Systems, Singapore Management University, Singapore
    [email protected]

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