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A Robust Algorithm of Encrypted Medical Image Retrieval Based on 3D DFT

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Smart Health (ICSH 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10219))

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Abstract

Cloud computing platform is not a fully trusted third party, which may leak the patient’s personal information when we store medical image, so we need to encrypt medical image. Meanwhile, in order to help doctors who can find out historical cases from the medical image database which are similar to the current diagnostic image to make more accurate diagnosis and treatment, this paper proposes an robust algorithm based on 3D DFT for encrypted medical image retrieval. At first, we extract feature vector of 3D encrypted image and establish features vector database. Next, the NC (Normalized Cross Correlation Coefficient) between the feature vector of query medical image and each one in the features vector database is computed automatically. Finally, the corresponding encrypted image with the highest NC value is returned. The results show that this algorithm has strong robustness against common attacks and geometric attacks.

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References

  1. Yang, L., et al.: A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 30–44 (2010)

    Article  Google Scholar 

  2. Rao, L.K., et al.: Content based medical image retrieval using local co-occurrence patterns. In: IEEE International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 743–748 (2015)

    Google Scholar 

  3. Jyothi, B., et al.: An effective multiple visual features for content based medical image retrieval. In: 9th International Conference on Intelligent Systems and Control (ISCO), pp. 1–5. IEEE (2015)

    Google Scholar 

  4. Yasmin, M., et al.: An efficient content based image retrieval using EI classification and color features. J. Appl. Res. Technol. 12(5), 877–885 (2014)

    Article  MathSciNet  Google Scholar 

  5. Wen, C.Y., Yao, J.Y.: Pistol image retrieval by shape representation. Forensic Sci. Int. 155(1), 35–50 (2005)

    Article  Google Scholar 

  6. Sun, J.D., et al.: Image retrieval based on color distribution entropy. Pattern Recogn. Lett. 27(10), 1122–1126 (2006)

    Article  Google Scholar 

  7. Kokare, M., et al.: Cosine-modulated wavelet based texture features for content-based image retrieval. Pattern Recogn. Lett. 25(4), 391–398 (2004)

    Article  Google Scholar 

  8. Smeulders, A.W.M., et al.: Content-based image retrieval at the end of early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000)

    Article  Google Scholar 

  9. Murthy, V.S., et al.: content based image retrieval using hierarchical and kmeans clustering techniques. Int. J. Eng. Sci. Technol. 2(3), 209–212 (2010)

    Google Scholar 

  10. Xia, Z.H., et al.: A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans. Inf. Forensics Secur. PP(99), 1 (2016)

    Google Scholar 

  11. Bellafqira, R., et al.: Content-based image retrieval in homomorphic encryption domain. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2944–2947 (2015)

    Google Scholar 

  12. Rivest, R., et al.: On data banks and privacy homomorphisms. In: Foundations of Secure Computation, pp. 169–180 (1978)

    Google Scholar 

  13. Palamakumbura, S., Usefi, H.: Database query privacy using homomorphic encryptions. In: IEEE 14th Canadian Workshop on Information Theory (CWIT), pp. 71–74 (2015)

    Google Scholar 

  14. Sun, Y., Wang, G.Y.: An image encryption scheme based on modified logistic map. In: Fourth International Workshop on Chaos-Fractals Theories and Applications (IWCFTA), pp. 179–182 (2011)

    Google Scholar 

  15. Zhang, X.F., Fan, J.L.: Extended logistic chaotic sequence and its performance analysis. Tsinghua Sci. Technol. 12(S1), 156–161 (2007)

    Article  Google Scholar 

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No: 61263033), and by the International Science and Technology Cooperation Project of Hainan (No: KJHZ2015-04, KJHZ2015-23) and the Institutions of Higher Learning Scientific Research Special Project of Hainan Province (NO: Hnkyzx2014-2).

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Authors and Affiliations

  1. College of Information Science and Technology, Hainan University, Haikou, China

    Shuangshuang Wang, Jingbing Li, Chunyan Zhang & Zhaohui Wang

Authors
  1. Shuangshuang Wang
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  2. Jingbing Li
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  3. Chunyan Zhang
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  4. Zhaohui Wang
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Corresponding author

Correspondence to Jingbing Li .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Chunxiao Xing

  2. Tsinghua University, Beijing, China

    Yong Zhang

  3. Beijing Foreign Studies University, Beijing, China

    Ye Liang

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Wang, S., Li, J., Zhang, C., Wang, Z. (2017). A Robust Algorithm of Encrypted Medical Image Retrieval Based on 3D DFT. In: Xing, C., Zhang, Y., Liang, Y. (eds) Smart Health. ICSH 2016. Lecture Notes in Computer Science(), vol 10219. Springer, Cham. https://doi.org/10.1007/978-3-319-59858-1_23

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  • DOI: https://doi.org/10.1007/978-3-319-59858-1_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59857-4

  • Online ISBN: 978-3-319-59858-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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