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Hybrid fragmentation of medical images’ attributes for multidimensional indexing

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Abstract

Medical images are growing dramatically both in quantity and application in the data era and the emerging Big Data problem. Searching and finding the proper medical image among such a huge number of medical images is not possible unless using a proper medical search engine. Indexing is the backend process in information retrieval systems in which documents are annotated with the index entry to be retrieved more accurately and efficiently. Most of the indexing techniques for medical images are content-based that are more complex and time consuming than the text-based ones. In this paper, a text-based medical image indexing technique is proposed that use medical images’ attributes and fragments them with the hybrid fragmentation approach (that are used in distributed database design) and re-form each of such attribute fragments into a hierarchy, constructing a multidimensional index. Hybrid fragmentation approach uses both Horizontal and vertical fragmentation of medical images’ attributes provided in header of medical image standard formats (such as the DICOM). Horizontal fragmentation uses values of image attributes (i.e., image content and properties dependent), whilst the vertical fragmentation uses pairwise affinity and correlation of the attributes in the application domain (i.e., application dependent). So, the proposed hybrid fragmentation approach based indexing of medical images aim to consider both the image properties and application statistics together to provide a better functionality. As the experimental performance evaluation results illustrate, the proposed multidimensional indexing can provide better precision of information retrieval rather that a single index or a set of multiple indexes, since that considers semantic relationship of the medical image’s attributes via the hybrid (horizontal and vertical) fragmentation. Moreover, the hybrid fragmentations approach based indexing also outperforms the vertical fragmentation-based multidimensional medical image indexing technique in terms of precision, recall and response time.

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References

  1. Hwang, K.H., Lee, H., Choi, D.: Medical image retrieval: past and present. Healthc Inform Res 18(1), 3–9 (2012)

    Article  Google Scholar 

  2. Schütze, H., Manning, C.D., Raghavan, P.: Introduction to Information Retrieval, vol. 39. Cambridge University Press (2008)

    MATH  Google Scholar 

  3. Ceri, S., Bozzon, A., Brambilla, M., Della Valle, E., Fraternali, P., Quarteroni, S.: Web Information Retrieval. Springer, Berlin (2013)

    Book  Google Scholar 

  4. Mosteghanemi, H., Drias, H.: Towards a multidimensional information retrieval. In: New Contributions in Information Systems and Technologies, pp. 91–100. Springer, Berlin (2015)

    Chapter  Google Scholar 

  5. Renita, D.B., Christopher, C.S.: Novel real time content based medical image retrieval scheme with GWO-SVM. Multimedia Tools Appl. 14, 1–7 (2020)

    Google Scholar 

  6. Srinivas, M.: Medical image indexing and retrieval using multiple features. pp. 365–369 (2013).

  7. Habibi Asl, S., Safaei, A.A.: Medical image retrieval approaches, methods and systems: a systematic review. Pajoohandeh J. 21(2), 61–73 (2016)

    Google Scholar 

  8. Cai, T. W., Kim, J., Feng, D. D.: Content-based medical image retrieval. In: Biomedical information technology. Academic Press, New York, pp. 83–113 (2008)

  9. Sudhamani, M.V., Venugopal, C.R.: Multidimensional indexing structures for content-based image retrieval: a survey. Int. J. Innov. Comput. Inf. Control 4(4), 867–882 (2008)

    Google Scholar 

  10. Kumar, A., Kim, J., Cai, W., Fulham, M., Feng, D.: Content-based medical image retrieval: a survey of applications to multidimensional and multimodality data. J. Digit. Imaging 26(6), 1025–1039 (2013)

    Article  Google Scholar 

  11. Das, P., Neelima, A.: An overview of approaches for content-based medical image retrieval. Int. J. Multimedia Inf. Retr. 6(4), 271–280 (2017)

    Article  Google Scholar 

  12. Owais, M., Arsalan, M., Choi, J., Park, K.R.: Effective diagnosis and treatment through content-based medical image retrieval (CBMIR) by using artificial intelligence. J. Clin. Med. 8(4), 462 (2019)

    Article  Google Scholar 

  13. Malviya, N., Choudhary, N., Jain, K.: Content based medical image retrieval and clustering based segmentation to diagnose lung cancer. Adv. Comput. Sci. Technol. 10(6), 1577–1594 (2017)

    Google Scholar 

  14. Kumar, M., Singh, K.M.: Retrieval of head–neck medical images using Gabor filter based on power-law transformation method and rank BHMT. SIViP 12(5), 827–833 (2018)

    Article  Google Scholar 

  15. Mezzoudj, S., Behloul, A., Seghir, R., Saadna, Y.: A parallel content-based image retrieval system using spark and tachyon frameworks. J. King Saud University-Computer Inf. Sci. 33(2), 141–149 (2019)

    Google Scholar 

  16. Siddiqa, A., Karim, A., Chang, V.: SmallClient for big data: an indexing framework towards fast data retrieval. Clust. Comput. 20(2), 1193–1208 (2017)

    Article  Google Scholar 

  17. Lan, R., Zhong, S., Liu, Z., Shi, Z., Luo, X.: A simple texture feature for retrieval of medical images. Multimedia Tools Appl. 77(9), 10853–10866 (2018)

    Article  Google Scholar 

  18. Özsu, M.T., Valduriez, P.: Distributed and Parallel Database Design. In Principles of Distributed Database Systems 2020 (pp. 33–89). Springer, Cham.

  19. Ahmed, K.T., Iqbal, M.A.: Region and texture based effective image extraction. Clust. Comput. 21(1), 493–502 (2018)

    Article  Google Scholar 

  20. Depeursinge, A., Miiller, H.: Medical visual information retrieval based on multi-dimensional texture modeling. Procedia Comput. Sci. 1(7), 127–129 (2011 Jan)

    Article  Google Scholar 

  21. Silberschatz, A., Korth, H.F., Sudarshan, S.: Database System Concepts, 7th edn. McGraw-Hill, New York (2020)

    MATH  Google Scholar 

  22. Kumar, S., Madria, S., Linderman, M.: M-Grid: a distributed framework for multidimensional indexing and querying of location based data. Distributed and Parallel Databases. 35(1), 55–81 (2017)

    Article  Google Scholar 

  23. Safaei, A.A., Habibi, A. S.: Multidimensional Indexing Technique for Medical Images Retrieval. J. Intell. Data Analysis (2021)

  24. Safaei, A., HabibiAsl, S.: Diamond: multi-dimensional indexing technique for medical images retrieval using vertical fragmentation approach. J. Supercomput. 4, 1–60 (2021)

    Google Scholar 

  25. Wan, S., Zhao, Y., Wang, T., Gu, Z., Abbasi, Q.H., Choo, K.K.: Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things. Futur. Gener. Comput. Syst. 1(91), 382–391 (2019)

    Article  Google Scholar 

  26. Elkariem, A.F., Bashir. M.B., Ahmed, T.H., Yousif, A.: Distributed medical image retrieval techniques: a review. In: IEEE 2017 Sudan Conference on Computer Science and Information Technology (SCCSIT), pp. 1–7 (2017)

  27. Özsu, M.T., Valduriez, P.: Principles of Distributed Database Systems. Springer, Berlin (2011)

    Google Scholar 

  28. Valduriez, P., Jimenez-Peris, R., Özsu, M.T.: Distributed database systems: the case for NewSQL. In: Transactions on Large-Scale Data-and Knowledge-Centered Systems XLVIII 2021, pp. 1–15. Springer, Berlin (2021)

  29. Nashat, D., Amer, A.A.: A comprehensive taxonomy of fragmentation and allocation techniques in distributed database design. ACM Comput. Surv. (CSUR) 51(1), 1–25 (2018)

    Article  Google Scholar 

  30. Ubaidillah, S.H., Ahmad, N.: Fragmentation techniques for ideal performance in distributed database—a survey. Int. J. Softw. Eng. Comput.Syst. 6(1), 18–24 (2020)

    Article  Google Scholar 

  31. Castro-Medina, F., Rodríguez-Mazahua, L., López-Chau, A., Cervantes, J., Alor-Hernández, G., Machorro-Cano, I.: Application of dynamic fragmentation methods in multimedia databases: a review. Entropy 22(12), 1352 (2020)

    Article  Google Scholar 

  32. Rinaldi, A.M., Russo, C.: User-centered information retrieval using semantic multimedia big data. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 2304–2313 (2018).

  33. MÜLLERac, H., Kalpathy-Cramer, J., Hersh, W., Geissbuhler, A.: Using Medline queries to generate image retrieval tasks for benchmarking. eHealth Beyond the Horizon–Get IT There:523 (2007)

  34. Tsikrika, T., Müller, H., Kahn Jr, C.E.: Log analysis to understand medical professionals’ image searching behaviour. In: Medical Informatics Europe (2012).

  35. http://lucene.apache.org

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  1. Department of Medical Informatics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran

    Ali Asghar Safaei

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  1. Ali Asghar Safaei
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Correspondence to Ali Asghar Safaei.

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Safaei, A.A. Hybrid fragmentation of medical images’ attributes for multidimensional indexing. Cluster Comput 25, 215–230 (2022). https://doi.org/10.1007/s10586-021-03356-7

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