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Gaussian Hermite polynomial based lossless medical image compression

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Abstract

The role of compression is inevitable in the storage and transmission of medical images. The polynomial based image compression is proposed in this work for the compression of abdomen CT medical images. The input images are preprocessed by min–max normalization; the pixels are scanned and subjected to polynomial approximation. The polynomial approximated coefficients are subjected to llyods quantization and encoded by arithmetic coder. The medical image compression using Gaussian Hermite polynomial gives superior results when compared with the legendre polynomial based image compression and JPEG lossless compression techniques in terms of Peak to signal noise ratio (PSNR), Mean square Error (MSE) and other picture quality metrics. The algorithms are tested on real-time DICOM abdomen CT image and can be used for data transfer in teleradiology application.

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Acknowledgements

The authors S. N. Kumar and Jins Sebastin would also like to acknowledge the support provided by Schmitt Centre for Biomedical Instrumentation (SCBMI) of Amal Jyothi College of Engineering.

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Authors and Affiliations

  1. Amal Jyothi College of Engineering, Kanjirappally, Kerala, India

    S. N. Kumar & Jins Sebastian

  2. Infant Jesus College of Engineering, Tirunelveli, India

    A. Ahilan

  3. Mar Ephraem College of Engineering and Technology, Elavuvilai, India

    Ajay Kumar Haridhas

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  1. S. N. Kumar
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  2. A. Ahilan
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  3. Ajay Kumar Haridhas
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  4. Jins Sebastian
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Correspondence to S. N. Kumar.

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Communicated by Y. Zhang.

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Kumar, S.N., Ahilan, A., Haridhas, A.K. et al. Gaussian Hermite polynomial based lossless medical image compression. Multimedia Systems 27, 15–31 (2021). https://doi.org/10.1007/s00530-020-00689-y

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