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RETRACTED ARTICLE: A discrete wavelet transform and recurrent neural network based medical image compression for MRI and CT images

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This article was retracted on 04 July 2022

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Abstract

Medical imaging is an active and developing field that has an impact on recognition, diagnosis and surgical planning of the disease. The image compression is introduced in the medical imaging for decreasing the redundancies to avoid storage and bandwidth related problems. In this research, Discrete Wavelet Transform and Recurrent Neural Network based compression techniques are used through brain images for achieving better compression rate with less loss. The region growing and otsu thresholding are used to separate the ROI image and Non ROI image. The DWT and RNN are used for the ROI and non ROI portions of the medical image. Additionally, the local attributes of RNN is enhanced by Gravitational Search Algorithm and Particle Swarm Optimization. The performance of this proposed method is calculated as peak signal to noise ratio, mean square error, compression ratio and space saving percentage. The existing techniques used to compare the proposed method are Fractional Order Darwinian PSO, Quasi Fractal and Oscillation Method and Burrows Wheeler Transform—move to front transform. The PSNR of the proposed method is 35.2362 dB which is high when compared to the existing methods.

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References

  • Ahilan A, Manogaran G, Raja C, Kadry S, Kumar SN, Kumar CA, Jarin T, Krishnamoorthy S, Kumar PM, Babu GC, Murugan NS (2019) Segmentation by fractional order darwinian particle swarm optimization based multilevel thresholding and improved lossless prediction based compression algorithm for medical images. IEEE Access 7:89570–89580

    Article  Google Scholar 

  • Ammah PNT, Owusu E (2019) Robust medical image compression based on wavelet transform and vector quantization. Inform Med Unlocked 15:100183

    Article  Google Scholar 

  • Amri H, Khalfallah A, Gargouri M, Nebhani N, Lapayre JC, Bouhlel MS (2017) Medical image compression approach based on image resizing, digital watermarking and lossless compression. J Signal Process Syst 87:203–214

    Article  Google Scholar 

  • Badshah G, Liew SC, Zain JM, Ali M (2016) Watermark compression in medical image watermarking using Lempel-Ziv-Welch (LZW) lossless compression technique. J Digit Imaging 29:216–225

    Article  Google Scholar 

  • Barros RS, Olabarriaga SD, Borst J, van Walderveen MA, Posthuma JS, Streekstra GJ, van Herk M, Majoie CB, Marquering HA (2016) Dynamic CT perfusion image data compression for efficient parallel processing. Med Biol Eng Comput 54:463–473

    Article  Google Scholar 

  • MedPix Database (2020). https://medpix.nlm.nih.gov. Accessed Jan 2020

  • Devadoss CP, Sankaragomathi B (2019) Near lossless medical image compression using block BWT–MTF and hybrid fractal compression techniques. Clust Comput 22:12929–12937

    Article  Google Scholar 

  • Flint AC (2012) Determining optimal medical image compression: psychometric and image distortion analysis. BMC Med Imaging 12:24

    Article  Google Scholar 

  • Juliet S, Rajsingh EB, Ezra K (2015) A novel image compression method for medical images using geometrical regularity of image structure. SIViP 9:1691–1703

    Article  Google Scholar 

  • Juliet S, Rajsingh EB, Ezra K (2015) Projection-based medical image compression for telemedicine applications. J Digit Imaging 28:146–159

    Article  Google Scholar 

  • Juliet S, Rajsingh EB, Ezra K (2016) A novel medical image compression using Ripplet transform. J Real Time Image Proc 11:401–412

    Article  Google Scholar 

  • Kasban H, Hashima S (2019) Adaptive radiographic image compression technique using hierarchical vector quantization and Huffman encoding. J Ambient Intell Humaniz Comput 10:2855–2867

    Article  Google Scholar 

  • Kumar SN, Fred AL, Kumar HA, Varghese PS (2019) Lossless compression of CT images by an improved prediction scheme using least square algorithm. Circ Syst Signal Process 39:522–542

    Article  Google Scholar 

  • Liu S, Bai W, Zeng N, Wang S (2019) A fast fractal based compression for MRI images. IEEE Access 7:62412–62420

    Article  Google Scholar 

  • Magar SS, Sridharan B (2020) Hybrid image compression technique using oscillation concept & quasi fractal. Health Technol 10:313–320

    Article  Google Scholar 

  • Manimekalai MAP, Vasanthi NA (2019) Hybrid Lempel–Ziv–Welch and clipped histogram equalization based medical image compression. Clust Comput 22:12805–12816

    Article  Google Scholar 

  • Rani MLP, Rao GS, Rao BP (2020) An efficient codebook generation using firefly algorithm for optimum medical image compression. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-01782-w

  • Reddy MR, Ravichandran KS, Venkatraman B, Sekar KR, Manikandan R (2018) Improved pharma education system in the field of medical images using compression techniques. Clust Comput 22:15049–15057

  • Selvi GUV, Nadarajan R (2017) CT and MRI image compression using wavelet-based contourlet transform and binary array technique. J Real Time Image Proc 13:261–272

    Article  Google Scholar 

  • Senapati RK, Prasad PK, Swain G, Shankar TN (2016) Volumetric medical image compression using 3D listless embedded block partitioning. SpringerPlus 5:2100

    Article  Google Scholar 

  • Song X, Huang Q, Chang S, He J, Wang H (2016) Novel near-lossless compression algorithm for medical sequence images with adaptive block-based spatial prediction. J Digit Imaging 29:706–715

    Article  Google Scholar 

  • Song X, Huang Q, Chang S, He J, Wang H (2018) Lossless medical image compression using geometry-adaptive partitioning and least square-based prediction. Med Biol Eng Comput 56:957–966

    Article  Google Scholar 

  • Song X, Liu B, Huang Q, Hu R (2019) Design of high-resolution quantization scheme with exp-Golomb code applied to compression of special images. J Vis Commun Image Represent 65:102684

    Article  Google Scholar 

  • Sunil H, Hiremath SG (2018) A combined scheme of pixel and block level splitting for medical image compression and reconstruction. Alex Eng J 57:767–772

    Article  Google Scholar 

  • UmaMaheswari S, SrinivasaRaghavan V (2020) Lossless medical image compression algorithm using tetrolet transformation. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-01792-8

  • Zhou W, Jia J, Su X (2018) A novel compression-driven lightweight framework for medical skeleton model visualization. IEEE Access 6:47627–47635

    Article  Google Scholar 

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Authors and Affiliations

  1. GITAM, Rushikonda, Andhra Pradesh, Visakhapatnam, India

    Saradha Rani Sabbavarapu

  2. Andhra University College of Engineering, AU, Visakhapatnam, Andhra Pradesh, India

    Sasibhushans Rao Gottapu

  3. JNTUK, Kakinada, Andhra Pradesh, India

    Prabhakara Rao Bhima

Authors
  1. Saradha Rani Sabbavarapu
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  2. Sasibhushans Rao Gottapu
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  3. Prabhakara Rao Bhima
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Correspondence to Saradha Rani Sabbavarapu or Prabhakara Rao Bhima.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04290-1"

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Sabbavarapu, S.R., Gottapu, S.R. & Bhima, P.R. RETRACTED ARTICLE: A discrete wavelet transform and recurrent neural network based medical image compression for MRI and CT images. J Ambient Intell Human Comput 12, 6333–6345 (2021). https://doi.org/10.1007/s12652-020-02212-7

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  • DOI: https://doi.org/10.1007/s12652-020-02212-7

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