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Vector quantizer of medical image using wavelet transform and enhanced SOM algorithm

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Abstract

Vector quantizer takes care of special image features like edges, and it belongs to the class of quantizers known as the second-generation coders. This paper proposes a novel vector quantization method using the wavelet transform and the enhanced SOM algorithm for the medical image compression. We propose the enhanced self-organizing algorithm to resolve the defects of the conventional SOM algorithm. The enhanced SOM, at first, reflects the error between the winner node and the input vector to the weight adaptation by using the frequency of the selection of the winner node. Secondly, it adjusts the weight in proportion to the present weight change and the previous one as well. To reduce the blocking effect and the computation requirement, we construct training image vectors involving image features by using the wavelet transform and apply the enhanced SOM algorithm to them for generating a well-defined codebook. Our experimental results have shown that the proposed method energizes the compression ratio and the decompression quality.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Silla University, Busan, South Korea

    Kwang-Baek Kim

  2. Department of Electrical Engineering, Pusan National University, Busan, South Korea

    Sungshin Kim

  3. Department of Internal Medicine, Pusan National University College of Medicine, Busan, South Korea

    Gwang-Ha Kim

Authors
  1. Kwang-Baek Kim
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  2. Sungshin Kim
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  3. Gwang-Ha Kim
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Corresponding author

Correspondence to Kwang-Baek Kim.

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Kim, KB., Kim, S. & Kim, GH. Vector quantizer of medical image using wavelet transform and enhanced SOM algorithm. Neural Comput & Applic 15, 245–251 (2006). https://doi.org/10.1007/s00521-006-0026-1

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  • DOI: https://doi.org/10.1007/s00521-006-0026-1

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