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Lossless Compression of CT Images by an Improved Prediction Scheme Using Least Square Algorithm


The storage and transmission of medical data such as CT/MR DICOM images are an essential part of the telemedicine application. In this paper, a prediction-based lossless compression algorithm using least square approach is proposed for the compression of CT images. Prior to compression, the preprocessing was performed by neutrosophic median filter. The gradient adjusted prediction scheme was employed for the determination of prediction coefficients, and polynomial least square fitting approach was used for optimal selection of prediction coefficients. The selected prediction coefficients are finally encoded by Huffman coder for transmission. The quality of the reconstructed image was validated by performance metrics and compared with other compression techniques like JPEG, contextual vector quantization and vector quantization using bat optimization (BAT-VQ). The proposed neutrosophic set-based least square compression algorithm was found to be efficient and tested on DICOM abdomen CT datasets. The hardware implementation was done by Raspberry Pi processor using Java platform for transferring the data through cloud network for telemedicine application.

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The authors would like to acknowledge the support provided by DST under IDP scheme (No: IDP/MED/03/2015). We thank Dr. P. Sebastian Varghese (consultant radiologist, Metro Scans & Laboratory, Trivandrum) for providing the medical CT images and supporting us in the preparation of the manuscript.

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Kumar, S.N., Fred, A.L., Kumar, H.A. et al. Lossless Compression of CT Images by an Improved Prediction Scheme Using Least Square Algorithm. Circuits Syst Signal Process 39, 522–542 (2020).

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  • Compression
  • Least square approach
  • Contextual vector quantization
  • Bat-CVQ
  • Codebook
  • Neutrosophic filter