Scalable medical data compression and transmission using wavelet transform for telemedicine applications

Wen Jyi Hwang; Ching Fung Chine; Kuo Jung Li

doi:10.1109/TITB.2003.808499

Scalable medical data compression and transmission using wavelet transform for telemedicine applications

Wen Jyi Hwang^*, Ching Fung Chine, Kuo Jung Li

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

46 Citations (Scopus)

Abstract

In this paper, a novel medical data compression algorithm, termed layered set partitioning in hierarchical trees (LSPIHT) algorithm, is presented for telemedicine applications. In the LSPIHT, the encoded bit streams are divided into a number of layers for transmission and reconstruction. Starting from the base layer, by accumulating bit streams up to different enhancement layers, we can reconstruct medical data with various signal-to-noise ratios (SNRs) and/or resolutions. Receivers with distinct specifications can then share the same source encoder to reduce the complexity of telecommunication networks for telemedicine applications. Numerical results show that, besides having low network complexity, the LSPIHT attains better rate-distortion performance as compared with other algorithms for encoding medical data.

Original language	English
Pages (from-to)	54-63
Number of pages	10
Journal	IEEE Transactions on Information Technology in Biomedicine
Volume	7
Issue number	1
DOIs	https://doi.org/10.1109/TITB.2003.808499
Publication status	Published - 2003 Mar
Externally published	Yes

Keywords

Image coding
Telemedical communication network
Wavelet transform

ASJC Scopus subject areas

Biotechnology
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TITB.2003.808499

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title = "Scalable medical data compression and transmission using wavelet transform for telemedicine applications",

abstract = "In this paper, a novel medical data compression algorithm, termed layered set partitioning in hierarchical trees (LSPIHT) algorithm, is presented for telemedicine applications. In the LSPIHT, the encoded bit streams are divided into a number of layers for transmission and reconstruction. Starting from the base layer, by accumulating bit streams up to different enhancement layers, we can reconstruct medical data with various signal-to-noise ratios (SNRs) and/or resolutions. Receivers with distinct specifications can then share the same source encoder to reduce the complexity of telecommunication networks for telemedicine applications. Numerical results show that, besides having low network complexity, the LSPIHT attains better rate-distortion performance as compared with other algorithms for encoding medical data.",

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T1 - Scalable medical data compression and transmission using wavelet transform for telemedicine applications

AU - Hwang, Wen Jyi

AU - Chine, Ching Fung

AU - Li, Kuo Jung

PY - 2003/3

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N2 - In this paper, a novel medical data compression algorithm, termed layered set partitioning in hierarchical trees (LSPIHT) algorithm, is presented for telemedicine applications. In the LSPIHT, the encoded bit streams are divided into a number of layers for transmission and reconstruction. Starting from the base layer, by accumulating bit streams up to different enhancement layers, we can reconstruct medical data with various signal-to-noise ratios (SNRs) and/or resolutions. Receivers with distinct specifications can then share the same source encoder to reduce the complexity of telecommunication networks for telemedicine applications. Numerical results show that, besides having low network complexity, the LSPIHT attains better rate-distortion performance as compared with other algorithms for encoding medical data.

AB - In this paper, a novel medical data compression algorithm, termed layered set partitioning in hierarchical trees (LSPIHT) algorithm, is presented for telemedicine applications. In the LSPIHT, the encoded bit streams are divided into a number of layers for transmission and reconstruction. Starting from the base layer, by accumulating bit streams up to different enhancement layers, we can reconstruct medical data with various signal-to-noise ratios (SNRs) and/or resolutions. Receivers with distinct specifications can then share the same source encoder to reduce the complexity of telecommunication networks for telemedicine applications. Numerical results show that, besides having low network complexity, the LSPIHT attains better rate-distortion performance as compared with other algorithms for encoding medical data.

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KW - Telemedical communication network

KW - Wavelet transform

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Scalable medical data compression and transmission using wavelet transform for telemedicine applications

Abstract

Keywords

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