Skip to main content

Advertisement

SpringerLink
Log in

DCT-based medical image compression using machine learning

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Medical images need to be efficiently compressed before transmission and storage, due to the storage capacity and constrained bandwidth issues. An ideal image compression system must yield a high compression ratio with good quality compressed images. Machine learning models are implemented to perform tasks, whereas humans have difficulties in completing. For instance, an optimum compression ratio could be suggested considering the details on an X-ray image. In this paper, machine learning algorithms are trained to relate the medical image contents to their compression ratio. Once trained, the optimum DCT compression ratio of the X-ray images is chosen upon presenting an image to the network. Experimental results showed that the radial basis function neural network learning algorithm can be efficiently used to classify the optimum compression ratio for the X-ray images while maintaining high image quality. The radial basis function neural network learning algorithm can be efficiently used to classify optimum compression ratio, considering optimum compression deviation with various levels of accuracy. The experiments are done using two compression scenarios considering the ratio of training and testing. Two different scenarios are defined and discussed. When proposed scenario 1 is considered, gradient boosting algorithm and support vector machine achieved the highest recognition rate of 79.16%; however, radial basis function neural network achieved the highest recognition rate of 90.625%, whereas when proposed scenario 2 considered with an accuracy rate of 89% as optimum compression deviation 1 is noted.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Ab Aziz, S., Sam, S.M., Mohamed, N., Sjarif, N.N.A., Baloch, J.: The comprehensive review of neural network: an intelligent medical image compression for data sharing. IJIE 12(7), 81–89 (2020)

    Google Scholar 

  2. Cheng, Z., Sun, H., Takeuchi, M., Katto, J.: Learning image and video compression through spatial-temporal energy compaction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10071–10080 (2019)

  3. Ibrahim, A.O., Ahmed, A., Abdu, A., Abd-alaziz, R., Alobeed, M.A., Saleh, A.Y., Elsafi, A.: Classification of mammogram images using radial basis function neural network. In: International Conference of Reliable Information and Communication Technology, pp. 311–320 (2019)

  4. Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., Lopez, A.: A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing 408, 189–215 (2020)

    Article  Google Scholar 

  5. Natekin, A., Knoll, A.: Gradient boosting machines, a tutorial. Front. Neurorobot. 7, 21 (2013)

    Article  Google Scholar 

  6. Khashman, A., Dimililer, K.: Medical radiographs compression using neural networks and haar wavelet. IEEE EUROCON 2009, 1448–1453 (2009)

    Google Scholar 

  7. Khashman, A., Dimililer, K.: Comparison criteria for optimum image compression. In: EUROCON 2005-The International Conference on Computer as a Tool, vol. 2, pp. 935–938 (2005)

  8. Kouanou, A.T., Tchiotsop, D., Tchinda, R., Tchapga, C.T., Telem, A.N.K., Kengne, R.: A machine learning algorithm for biomedical images compression using orthogonal transforms. Int. J. Image Graph. Signal Process. 10(11), 38 (2018)

    Article  Google Scholar 

  9. Shukla, S., Srivastava, A.: Medical images Compression using convolutional neural network with LWT. Int. J. Mod. Commun. Technol. Res. 12(7), 265086 (2018)

    Google Scholar 

  10. Hosny, K.M., Khalid, A.M., Mohamed, E.R.: Optimized medical image compression for telemedicine applications. Artif. Intell. Data Min. Healthc. 119–142 (2021)

  11. Khashman, A., Dimililer, K.: Haar image compression using a neural network. In: Proceedings of the WSEAS International Applied Computing Conference (ACC’08) (2008)

  12. Brownlee, J.: A gentle introduction to xgboost for applied machine learning. Machine Learning Mastery (2016)

  13. Al-Rababah, M., Al-Marghirani, A.: Implementation of novel medical image compression using artificial intelligence. Int. J. Adv. Comput. Sci. Appl. 7(5), 328–332 (2016)

    Google Scholar 

  14. Mody, D., Prajapati, P., Thaker, P., Shah, N.: Image compression using DWT and optimization using evolutionary algorithm. SSRN 3568590 (2020)

  15. Golts, A., Schechner, Y.Y.: Image compression optimized for 3D reconstruction by utilizing deep neural networks. arXiv preprint 12618 (2003)

  16. Artusi, A., Mantiuk, R.K., Richter, T., Hanhart, P., Korshunov, P., Agostinelli, M., Ebrahimi, T.: Overview and evaluation of the JPEG XT HDR image compression standard. J. Real-Time Image Process. 16(2), 413–428 (2019)

    Article  Google Scholar 

  17. Song, J., He, T., Gao, L., Xu, X., Hanjalic, A., Shen, H.T.: Unified binary generative adversarial network for image retrieval and compression. Int. J. Comput. Vis. 26, 1–22 (2020)

    MathSciNet  Google Scholar 

  18. Shukla, S., Srivastava, A.: Medical images compression using convolutional neural network with LWT. Int. J. Mod. Commun. Technol. Res. 6(6), 265086 (2018)

    Google Scholar 

  19. Tan, L., Zeng, Y., Zhang, W.: Research on image compression coding technology. J. Phys. Conf. Ser. 1284(1), 012069 (2019)

    Article  Google Scholar 

  20. Khashman, A., Dimililer, K.: Image compression using neural networks and Haar wavelet. WSEAS Trans. Signal Process. 4(5), 330–339 (2008)

    Google Scholar 

  21. Kaur, A., Jindal, B.: Image compression using decision tree technique. Int. J. Adv. Res. Comput. Sci. 8, 8 (2017)

    Google Scholar 

  22. Hajjaji, M.A., Dridi, M., Mtibaa, A.: A medical image crypto-compression algorithm based on neural network and PWLCM. Multimedia Tools Appl. 78(11), 14379–14396 (2019)

    Article  Google Scholar 

  23. Li, W., Sun, W., Zhao, Y., Yuan, Z., Liu, Y.: Deep image compression with residual learning. Appl. Sci. 10(11), 4023 (2020)

    Article  Google Scholar 

  24. Fu, H., Liang, F., Lei, B.: An extended hybrid image compression based on soft-to-hard quantification. IEEE Access 8, 95832–95842 (2020)

    Article  Google Scholar 

  25. Dimililer, K.: Back-propagation neural network implementation for medical image compression. J. Appl. Math. (2013)

  26. Perumal, B., Rajasekaran, M.P.: A hybrid discrete wavelet transform with neural network back propagation approach for efficient medical image compression. In: 2016 International Conference on Emerging Trends in Engineering, Technology and Science, pp. 1–5 (2016)

  27. Dimililer, K., Kiani, E.: Application of back propagation neural networks on maize plant detection. Procedia Comput. Sci. 120, 376–381 (2017)

    Article  Google Scholar 

  28. Dash, C.S.K., Behera, A.K., Dehuri, S., Cho, S.B.: Radial basis function neural networks: a topical state-of-the-art survey. Open Comput. Sci. 1 (2016)

  29. Dimililer, K., Zarrouk, S.: ICSPI: intelligent classification system of pest insects based on image processing and neural arbitration. Appl. Eng. Agric. 33(4), 453 (2017)

    Article  Google Scholar 

  30. Oytun, M., Tinazci, C., Sekeroglu, B., Acikada, C., Yavuz, H.U.: Performance prediction and evaluation in female handball players using machine learning models. IEEE Access 8, 116321–116335 (2020)

    Article  Google Scholar 

  31. Yuan, Z., Wang, C.: An improved network traffic classification algorithm based on Hadoop decision tree. In: 2016 IEEE International Conference of Online Analysis and Computing Science, pp. 53–56 (2016)

  32. Bentaouza, C.M., Benyettou, M.: Support vector machine applied to compress medical image. JCP 13(5), 580–587 (2018)

  33. Seo, H., Badiei Khuzani, M., Vasudevan, V., Huang, C., Ren, H., Xiao, R., Xing, L.: Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state-of-art applications. Med. Phys. 47(5), 148–167 (2020)

    Article  Google Scholar 

  34. Batra, R., Khatri, I.: Image compression using discrete wavelet transform approach. Int. J. Res. Appl. Sci. Eng. Technol. 5, 1755–1761 (2017)

    Google Scholar 

  35. Kiernan, D.: Correlation and simple linear regression. Nat. Resour. Biom. 150–181 (2014)

  36. Kim, M., Yun, J., Cho, Y., Shin, K., Jang, R., Bae, H.J., Kim, N.: Deep learning in medical imaging. Neurospine 17(2), 471 (2020)

    Article  Google Scholar 

  37. Ji, X., Yang, B., Tang, Q.: Acoustic seabed classification based on multibeam echosounder backscatter data using the PSO-BP-AdaBoost algorithm: a case study from Jiaozhou Bay. IEEE J. Oceanic Eng. (2020)

  38. Amirjanov, A., Dimililer, K.: Image compression system with an optimization of compression ratio. IET Image Process. 13(1), 1960–1969 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical & Electronic Engineering, Faculty of Engineering, Research Center for AI and IoT, Near East University, via Mersin 10, Nicosia, North Cyprus, Turkey

    Kamil Dimililer

Authors
  1. Kamil Dimililer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kamil Dimililer.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dimililer, K. DCT-based medical image compression using machine learning. SIViP 16, 55–62 (2022). https://doi.org/10.1007/s11760-021-01951-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-021-01951-0

Keywords

Search

Navigation