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Towards professionally user-adaptive large medical image transmission processing in mobile telemedicine systems

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Abstract

To effectively and efficiently reduce the transmission costs of large medical image in (mobile) telemedicine systems, we design and implement a professionally user-adaptive large medical image transmission method called UMIT. Before transmission, a preprocessing step is first conducted to obtain the optimal image block (IB) replicas based on the users’ professional preference model and the network bandwidth at a master node. After that, the candidate IBs are transmitted via slave nodes according to the transmission priorities. Finally, the IBs can be reconstructed and displayed at the users’ devices. The proposed method includes three enabling techniques: (1) user’s preference degree derivation of the medically useful areas, (2) an optimal IB replica storage scheme, and (3) an adaptive and robust multi-resolution-based IB replica selection and transmission method. The experimental results show that the performance of our proposed UMIT method is both efficient and effective, minimizing the response time by decreasing the network transmission cost.

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Notes

  1. Strictly speaking, the professional preference refers to the organ(s) that a physician is specialized (or interested) in.

References

  1. Qureshi, A., Shoeb, A., Guttag, J.: Building a high-quality mobile telemedicine system using network striping over dissimilar wireless wide area networks. In: Proc. of Annual Int’l Conf. on IEEE Engineering in Medicine and Biology Society, vol. 4, pp. 3942–3945 (2005)

  2. Maani, R., Camorlinga, S., Arnason, N.: A parallel method to improve medical image transmission. J. Digit Imaging 25(1), 101–109 (2012)

    Article  Google Scholar 

  3. Wang, W., Zhao, M., Wang, H., Hua, K.: Exploring region of interest (ROI) to support quality of service in unreliable wireless electronic healthcare communications. Int. J. Healthc. Inf. Syst. Inf. 7(4), 1–12 (2012)

    Article  Google Scholar 

  4. Charles, J.T., Larry, L.P.: Image transfer: an end-to-end design. In: ACM SIGCOMM Int’l Conference on Data Communication, pp. 258–268 (1992)

  5. John, M.D., Georey, M.D., Song, X.Y.: Fast lossy internet image transmission. In: ACM Int’l Conference on Multimedia (1995)

  6. Raman, S., Balakrishnan, H., Srinivasan, M.: An image transport protocol for the Internet. In: Int’l Conference on Network Protocol, pp. 209–219 (2000)

  7. Allcocka, B., Bestera, J., Bresnahan, J., et al.: Data management and transfer in high-performance computational grid environments. Parallel Comput. 28(5), 749–771 (2002)

  8. Lin, T., Hao, P.: Compound image compression for real-time computer screen image transmission. IEEE Trans. Image Process. 14(8), 993–1005 (2005)

    Article  MathSciNet  Google Scholar 

  9. Ruiz, V.G., Fernández, J.J., García, I.: Image compression for progressive transmission. In: In the Nineteenth IASTED Int’l Conference on Applied Informatics: Advances in Computer Applications, Innsbruck, pp. 519–524 (2001)

  10. Gao, D.H., Liu, D.H., Feng, Y.Q., et al.: A robust image transmission scheme for wireless channels based on compressive sensing. In: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. Lecture Notes in Computer Science, vol. 6216, pp. 334–341 (2010)

  11. Chang, R.C., Shih, T.K., Hsu, H.H.: A strategic decomposition for adaptive image transmission. J. Inf. Sci. Eng. 24(3), 691–707 (2008)

    Google Scholar 

  12. Chang, C.C., Shine, F.C., Chen, T.S.: A new scheme of progressive image transmission based on bit-plane method. In: Asia-Pacific Conference on Communications and Fourth Optoelectronics and Communications Conference, vol. 2, pp. 892–895 (1999)

  13. Chang, C.C., Shih, T.K., Lin, I.C.: An efficient progressive image transmission method based on guessing by neighbors. Vis. Comput. Int. J. Comput. Graph. 18, 341–353 (2002)

    Google Scholar 

  14. Chang, C.C., Wu, M.N.: A color image progressive transmission method by common bit map block truncation coding approach. In: Int’l Conference on Communication Technology, vol. 2, pp. 1774–1778 (2003)

  15. Kim, J.H., Song, W.J.: Pyramid-structured progressive image transmission using quantization error delivery in transform domains. IEE Vis. Image Signal Process. 143, 132–136 (1996)

    Article  Google Scholar 

  16. Tzou, K.H.: Progressive image transmission: a review and comparison of techniques. Opt. Eng. 26, 581–589 (1987)

    Article  Google Scholar 

  17. Boluk, P.S., Baydere, S., Emre Harmanci, A.: Robust image transmission over wireless sensor networks. J. Mob. Netw. Appl. 16(2), 149–170 (2011)

  18. Aziz, S.M., Pham, D.M.: Energy efficient image transmission in wireless multimedia sensor networks. IEEE Commun. Lett. 17(6), 1084–1087 (2013)

    Article  Google Scholar 

  19. Sun, Y., Xiong, Z.-X.: Progressive image transmission over space-time coded OFDM-based MIMO systems with adaptive modulation. IEEE Trans. Mob. Comput. 5(8), 1016–1028 (2006)

    Article  Google Scholar 

  20. Victor, S., Abugharbieh, R., Nasiopoulos, P.: 3-D scalable medical image compression with optimized volume of interest coding. IEEE Trans. Med. Imaging 29(10), 1808–1820 (2010)

    Article  Google Scholar 

  21. Arslan, S.S., Cosman, P.C., Milstein, L.B.: Generalized unequal error protection LT Codes for progressive data transmission. IEEE Trans. Image Process. 21(8), 3586–3597 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  22. Zhuang, Y., Jiang, N., Wu, Z., Li, Q., et al.: Efficient and robust large medical image retrieval in mobile cloud computing environment. Inf. Sci. (INS) 263, 60–86 (2014)

  23. Hoppe, H.: Progressive meshes. In: Proceedings of SIGGRAPH’96, pp 77–108 (1996)

  24. Teler, E., Lischinski, D.: Streaming of complex 3d scenes for remote walkthroughs. In: Computer Graphics Forum (Eurographics 2001 Conference Proceedings), vol. 20, no. 3, pp. 17–25 (2001)

  25. Rusinkiewicz, S., Levoy, M.: Streaming qsplat: a viewer for networked visualization of large, dense models. In: ACM Interactive 3D 2001 Conference Proceedings, pp. 63–69 (2001)

  26. Gu, X., Gortler, S.J., Hoppe, H.: Geometry images. ACM Trans. Graph. 21(3), 355–361 (2002)

    Article  Google Scholar 

  27. Bischoff, S., Kobbelt, L.: Towards robust broadcasting of geometric data. Comput. Graph. 26, 665–675 (2002)

    Article  Google Scholar 

  28. Lamberti, F., Sanna, A.: A streaming-based solution for remote visualization of 3D graphics on mobile devices. IEEE Trans. Vis. Comput. Graph. 13(2), 247–260 (2007)

    Article  Google Scholar 

  29. Cheng, W., Ooi, W.T., Mondet, S., Grigoras, R., Morin, G.: An analytical model for progressive mesh streaming. In: In the 15th ACM International Multimedia Conference, September 24–29, pp 737–746(2007)

  30. Kumar, S., Mishra, M.K.: 3D object transmission over internet using 2D image streaming technology. Int. J. Adv. Res. Electron. Commun. Eng. (IJARECE), 2(4), 410–414 (2013)

    Google Scholar 

  31. Ester, M., Kriegel, H.-P., Sander, J., Xu, X.W.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. of the Second Int’l Conference on Knowledge Discovery and Data Mining (KDD-96), pp. 226–231 (1996)

  32. Gal, V., Kerre, E., Tikk, D.: Organ detection in medical images with discriminately trained deformable part model. In: Prof. of 2013 IEEE 9th Int’l Conf. on Computational Cybernetics (ICCC), pp. 153–157 (2013)

  33. The medical image dataset. http://www.ece.ncsu.edu/imaging/Archives/ImageDataBase/Medical/index.html (2009)

  34. The Android platform http://www.google.com/android (2010)

  35. MySQL http://www.mysql.com/ (2010)

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Acknowledgments

The authors would like to thank the editors and anonymous reviewers for their helpful comments. This work is partially supported by the Program of the National Natural Science Foundation of China under Grant Nos. 61272188, 61540064, 61379075; the Ministry of Education of Humanities and Social Sciences Project under Grant No. 14YJCZH235; the “Qianjiang Talent” Project of Zhejiang Province under Grant No. QJD1402017; the National Science & Technology Pillar Program under Grant No. 2014BAK14B01; and the Program of Natural Science Foundation for Distinguished Young Scholars of Zhejiang Province.

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Authors and Affiliations

  1. College of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, People’s Republic of China

    Yi Zhuang

  2. Hangzhou First People’s Hospital, Hangzhou, People’s Republic of China

    Nan Jiang

  3. Department of Computer Science, City University of Hong Kong, Hong Kong, People’s Republic of China

    Qing Li

  4. School of Computer, Hangzhou Dianzi University, Hangzhou, People’s Republic of China

    Hua Hu

  5. Faculty of Education, The University of Hong Kong, Hong Kong, People’s Republic of China

    Dickson K. W. Chiu

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  1. Yi Zhuang
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  3. Qing Li
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  4. Hua Hu
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Correspondence to Yi Zhuang.

Additional information

Communicated by B. Prabhakaran.

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Zhuang, Y., Jiang, N., Li, Q. et al. Towards professionally user-adaptive large medical image transmission processing in mobile telemedicine systems. Multimedia Systems 24, 123–145 (2018). https://doi.org/10.1007/s00530-016-0526-5

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