Compression of CT Images using Contextual Vector Quantization with Simulated Annealing for Telemedicine Application.

The role of compression is vital in telemedicine for the storage and transmission of medical images. This work is based on Contextual Vector Quantization (CVQ) compression algorithm with codebook optimization by Simulated Annealing (SA) for the compression of CT images. The region of interest (foreg...

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Publicado en:Journal of Medical Systems Vol. 42; no. 11; pp. 1 - 2
Autores principales: Kumar, S. N., Lenin Fred, A., Sebastin Varghese, P.
Formato: algorithm computer program diagnostic images equations & formulas research tables/charts Journal Article
Publicado: Springer Nature Nov2018
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Nov2018
      vid: 42
      iid: 11
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-018-1090-7
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        atl: Compression of CT Images using Contextual Vector Quantization with Simulated Annealing for Telemedicine Application.
      aug:
        au:
          Kumar, S. N.
          Lenin Fred, A.
          Sebastin Varghese, P.
        affil: Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Rajiv Gandhi Salai, 600 119, Chennai, Tamil Nadu, India
      sug:
        subj:
          Image Processing, Computer Assisted Methods
          Tomography, X-Ray Computed
          Telemedicine
          Digital Compression Methods
          Simulations
          Human
          Mann-Whitney U Test
          Diagnostic Imaging
          India
          Nonparametric Statistics
          Descriptive Statistics
          T-Tests
          Teleradiology
          Image Enhancement
      ab: The role of compression is vital in telemedicine for the storage and transmission of medical images. This work is based on Contextual Vector Quantization (CVQ) compression algorithm with codebook optimization by Simulated Annealing (SA) for the compression of CT images. The region of interest (foreground) and background are separated initially by region growing algorithm. The region of interest is encoded with low compression ratio and high bit rate; the background region is encoded with high compression ratio and low bit rate. The codebook generated from foreground and background is merged, optimized by simulated annealing algorithm. The performance of CVQ-SA algorithm was validated in terms of metrics like Peak to Signal Noise Ratio (PSNR), Mean Square Error (MSE) and Compression Ratio (CR), the result was superior when compared with classical VQ, CVQ, JPEG lossless and JPEG lossy algorithms. The algorithms are developed in Matlab 2010a and tested on real-time abdomen CT datasets. The quality of reconstructed image was also validated by metrics like Structural Content (SC), Normalized Absolute Error (NAE), Normalized Cross Correlation (NCC) and statistical analysis was performed by Mann Whitney U Test. The outcome of this work will be an aid in the field of telemedicine for the transfer of medical images.
      pubtype: Academic Journal
      doctype:
        algorithm
        computer program
        diagnostic images
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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