DCT-based medical image compression using machine learning


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.

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  1. 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. 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. 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. 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. 5.

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

    Article  Google Scholar 

  6. 6.

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

    Google Scholar 

  7. 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. 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. 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. 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. 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. 12.

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

  13. 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. 14.

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

  15. 15.

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

  16. 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. 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. 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. 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. 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. 21.

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

    Google Scholar 

  22. 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. 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. 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. 25.

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

  26. 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. 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. 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. 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. 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. 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. 32.

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

  33. 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. 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. 35.

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

  36. 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. 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. 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 

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Correspondence to Kamil Dimililer.

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Dimililer, K. DCT-based medical image compression using machine learning. SIViP (2021). https://doi.org/10.1007/s11760-021-01951-0

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  • Optimum image compression
  • Machine learning
  • DCT image compression
  • Medical imaging