Skip to main content
Log in

Feed-Forward Neural Network-Based Predictive Image Coding for Medical Image Compression

  • Research Article - Special Issue - Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

The generation of high volume of medical images in recent years has increased the demand for more efficient compression methods to cope up with the storage and transmission problems. In the case of medical images, it is important to ensure that the compression process does not affect the image quality adversely. In this paper, a predictive image coding method is proposed which preserves the quality of the medical image in the diagnostically important region (DIR) even after compression. In this method, the image is initially segmented into two portions, namely DIR and non-DIR portions, using a graph-based segmentation procedure. The prediction process is implemented using two identical feed-forward neural networks (FF-NNs) at the compression and decompression stages. Gravitational search and particle swarm algorithms are used for training the FF-NNs. Prediction is performed in both a lossless (LLP) and near-lossless (NLLP) manner for evaluating the performances of the two FF-NN training algorithms. The prediction error sequence which is the difference between the actual and predicted pixel values is further compressed using a Markov model-based arithmetic coding. The proposed method is tested using CLEF med 2009 database. The experimental results demonstrate that the proposed method is equipped for compressing the medical images with minimum degradation in the image quality. It is found that the gravitational search method achieves higher PSNR values compared to the particle swarm and backpropagation methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Bairagi, V.K.; Sapkal, A.M.: ROI based DICOM image compression for telemedicine. Digit. Image Process. 3(11), 662–666 (2011)

    Google Scholar 

  2. Kaur, M.; Wasson, V.: ROI based medical image compression for telemedicine application. Proc. Comput. Sci. 70, 579–585 (2015)

    Article  Google Scholar 

  3. Zhu, Y.; Yuan, J.A.: Bit allocation optimization method for ROI based image compression with stable image quality. In: IEEE 22nd International Conference on Pattern Recognition (ICPR), pp. 849-854 (2014)

  4. Felzenszwalb, P.F.; Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)

    Article  Google Scholar 

  5. Duan, L.; Liao, X.; Xiang, T.: A secure arithmetic coding based on Markov model. Commun. Nonlinear Sci. Numer. Simul. 16(6), 2554–2562 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  6. Singh, S.; Vinod, K.; Verma, H.K.: Adaptive threshold based block classification in medical image compression for teleradiology. Comput. Biol. Med. 37, 811–819 (2007)

    Article  Google Scholar 

  7. Jyotheswar, J.; Mahapatra, S.: Efficient FPGA implementation of DWT and modified SPIHT for lossless image compression. J. Syst. Archit. 53, 369–378 (2007)

    Article  Google Scholar 

  8. Bernab, G.; García, J.M.; González, J.: A lossy 3D wavelet transform for high-quality compression of medical video. J. Syst. Softw. 82, 526–534 (2009)

    Article  Google Scholar 

  9. Chen, Y.-Y.: Medical image compression using DCT-based sub band decomposition and modified SPIHT data organization. Int. J. Med. Inf. 76, 717–725 (2007)

    Article  Google Scholar 

  10. Srikanth, R.; Ramakrishnan, A.G.: Contextual encoding in uniform and adaptive mesh based lossless compression of MR images. IEEE Trans. Med. Imaging 24, 1199–1206 (2005)

    Article  Google Scholar 

  11. Saremi, S.; Mirjalili, S.; Lewis, A.: How important is a transfer function in discrete heuristic algorithms. Neural Comput. Appl. 26(3), 625–640 (2015)

    Article  Google Scholar 

  12. Farahani, S.F.; Sheikhan, M.; Farrokhi, A.: Facial emotion recognition using gravitational search algorithm for colored images. In: International Symposium on Artificial Intelligence and Signal Processing, Springer International Publishing, pp. 32–40 (2013)

  13. Jha, G.K.; Thulasiraman, P.; Thulasiram, R.K.: PSO based neural network for time series forecasting. In: IEEE International Joint Conference on Neural Networks, pp. 1422–1427 (2009)

  14. Kennedy, J.; Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Perth, Australia (1995)

  15. Yeo, W.K.; Yap, D.F.W.; Oh, T.H.; Andito, D.P.; Kok, S.L.; Ho, Y.H.; Suaidi, M.K.: Grayscale medical image compression using feed forward neural networks. In: International Conference on Computer Applications and Industrial Electronics, pp. 633–638 (2011)

  16. Sau, K.; Basak, R.K.; Chanda, A.: Image compression based on block truncation coding using clifford algebra. Proc. Technol. 10, 699–706 (2013)

    Article  Google Scholar 

  17. Ahamed, A.M.U.; Eswaran, C.; Kannan, R.: CBIR system based on prediction errors. J. Inf. Sci. Eng. 33(2), 347–365 (2017)

    MathSciNet  Google Scholar 

  18. Trelea, I.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Process. Lett. 85, 317–325 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  19. Deserno, T.M.; Antani, S.; Rodney Long, L.: Content-based image retrieval for scientific literature access. Methods Inf. Med. 48(4), 371 (2009)

    Article  Google Scholar 

  20. Ahamed, A.M.U.; Eswaran, C.; Kannan, R.: Predictive medical image compression using neural networks with gravitational search and particle swarm algorithms. In: 20th International Workshop on Advanced Image Technology, Penang, Malaysia (2017)

  21. Wu, X.; Memon, N.: Context-based, adaptive, lossless image coding. IEEE Trans. Commun. 45(4), 437–444 (1997)

    Article  Google Scholar 

  22. Liu, W.; Zeng, W.; Dong, L.; Yao, Q.: Efficient compression of encrypted grayscale images. IEEE Trans. Image Process. 19(4), 1097–1102 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  23. Zhou, J.; Liu, X.; Au, O.C.; Tang, Y.Y.: Designing an efficient image encryption-then compression system via prediction error clustering and random permutation. IEEE Trans. Inf. Forensics Secur. 9(1), 39–50 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohamed Uvaze Ahamed Ayoobkhan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ayoobkhan, M.U.A., Chikkannan, E. & Ramakrishnan, K. Feed-Forward Neural Network-Based Predictive Image Coding for Medical Image Compression. Arab J Sci Eng 43, 4239–4247 (2018). https://doi.org/10.1007/s13369-017-2837-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-017-2837-z

Keywords

Navigation