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Bat Optimization Based Vector Quantization Algorithm for Medical Image Compression

Part of the Intelligent Systems Reference Library book series (ISRL,volume 150)

Abstract

Image compression plays a significant role in medical data storage and transmission. The lossless compression algorithms are generally preferred for medical images. The variants of lossy vector quantization algorithm are also used in many cases, where the reconstructed image quality is fairly good with optimum compression ratio. Bat optimization algorithm is formulated based on the biological trait of bats to detect prey and avoid obstacles by using echolocation. In this chapter, the application of bat optimization algorithm in medical image compression is highlighted. The bat optimization algorithm is employed here for the optimum codebook design in Vector Quantization (VQ) algorithm. The performance of the BAT-VQ compression scheme was compared with the Classical VQ, Contextual Vector Quantization (CVQ) and JPEG lossless schemes for the abdomen CT images. Satisfactory results were obtained by BAT-VQ in terms of picture quality measures.

Keywords

  • Segmentation
  • Bat optimization algorithm
  • Compression
  • Vector quantization
  • Contextual vector quantization

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Acknowledgements

The authors would like to acknowledge the support provided by DST under IDP scheme (No: IDP/MED/03/2015). We thank Dr. 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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Correspondence to A. Lenin Fred .

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Fred, A.L., Kumar, S.N., Ajay Kumar, H., Abisha, W. (2019). Bat Optimization Based Vector Quantization Algorithm for Medical Image Compression. In: Hemanth, J., Balas, V. (eds) Nature Inspired Optimization Techniques for Image Processing Applications. Intelligent Systems Reference Library, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-319-96002-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-96002-9_2

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