Advertisement

Cluster Computing

, Volume 22, Supplement 1, pp 1345–1359 | Cite as

Cluster based-image descriptors and fractional hybrid optimization for medical image retrieval

  • Poonam YadavEmail author
Article

Abstract

Nowadays health information management (HIM) is a challenging area of research. In HIM, retrieving, storing and interpreting the information regarding the health of patients are considered as the significant stages. As a consequence, retrieving the earlier records of the case, based on the current information of patients helps in assisting medical practitioners in recognition of patients with similar problems and the curing process. On focusing this as an important objective of this study, an image retrieval system is proposed which utilizes visual features to describe the contents of the image. Initially, the input images associated with cases of patients are considered as input. Then, the features, such as Correlogram, LGP, wavelet moments and mean, variance, skew, kutoiss from BFC of the image are detected by the exploitation of image descriptors, and they are stored in the feature database. Then, various weights are allocated to every feature, and the Fractional hybrid optimization is proposed by merging fractional brain storm optimization (FBSO) with fractional lion algorithm (FLA) for optimal weight score generation. The simulation is done with six forms of medical images and the parameters, such as recall, precision and f-measure are utilized for distinguishing the performance of the conventional methods.

Keywords

Medical image retrieval Case-based reasoning Bayesian fuzzy clustering Image descriptors Optimization Fractional calculus 

References

  1. 1.
    Khansa, L., Davis, Z., Davis, H., Chin, A., MacMichael, N.: Health information technologies for patients with diabetes. Technol. Soc. 4, 1–94 (2016)CrossRefGoogle Scholar
  2. 2.
    Rippen, H.E., Pan, E.C., Russell, C., Byrne, C.M., Swift, E.K.: Organizational framework for health information technology. Int. J. Med. Inf. 82(4), e1–e13 (2013)CrossRefGoogle Scholar
  3. 3.
    Depeursinge, A., Duc, S., Eggel, I., Müller, H.: Mobile medical visual information retrieval. IEEE Trans. Inf. Technol. Biomed. 16, (1), 53–61 (2012)Google Scholar
  4. 4.
    Lisa, L.M.: Ethics and subsequent use of electronic health record data. J. Biomed. Inf. 71, 143–146 (2017)CrossRefGoogle Scholar
  5. 5.
    Moskovitch, R., Polubriaginof, F., Weiss, A., Ryan, P., Tatonetti, N.: Procedure prediction from symbolic electronic health records via time intervals analytics. J. Biomed. Inf. 75, 70–82 (2017)CrossRefGoogle Scholar
  6. 6.
    Zhang, J., Xu, W., Guo, J., Gao, S.: A temporal model in electronic health record search. Knowl.-Based Syst. 126, 56–67 (2017)Google Scholar
  7. 7.
    Spil, T.A.M., Cellucci, L.W.: Electronic health records across the nations. Health Policy Technol. 4(2), 89–90 (2015)CrossRefGoogle Scholar
  8. 8.
    Penrod, L.E.: Electronic health record transition considerations. PM&R 9(5), s13–s18 (2017)CrossRefGoogle Scholar
  9. 9.
    Kang, Y.-B., Krishnaswamy, S., Zaslavsky, A.: A retrieval strategy for cbr using similarity and association knowledge. IEEE Trans. Cybernet. 44(4), 473–487 (2014)CrossRefGoogle Scholar
  10. 10.
    Qayyum, A., Anwar, S.M., Awais, M., Majid, M.: Medical image retrieval using deep convolutional neural network. Neurocomputing 266, 8–20 (2017)CrossRefGoogle Scholar
  11. 11.
    Piras, L., Giacinto, G.: Information fusion in content-based image retrieval: a comprehensive overview. Inf. Fusion 37, 50–60 (2017)CrossRefGoogle Scholar
  12. 12.
    Markonis, D., Holzer, M., Baroz, F., Castaneda, R.L.R.D., Müller, H.: User-oriented evaluation of a medical image retrieval system for radiologists. Int. J. Med. Inf. 84(10), 774–783 (2015)CrossRefGoogle Scholar
  13. 13.
    Wissow, L.S., Brown, J.D., Hilt, R.J., Sarvet, B.D.: Evaluating integrated mental health care programs for children and youth. Child Adolesc. Psychiatr. Clin. North Am. 26(4), 795–814 (2017)CrossRefGoogle Scholar
  14. 14.
    Poudel, P., Griffiths, R., Wong, V.W., Arora, A., George, A.: Knowledge and practices of diabetes care providers in oral health care and their potential role in oral health promotion: a scoping review. Diabetes Res. Clin. Pract. 130, 266–277 (2017)CrossRefGoogle Scholar
  15. 15.
    Muriana, C., Piazza, T., Vizzini, G.: An expert system for financial performance assessment of health care structures based on fuzzy sets and KPIs. Knowl.-Based Syst. 97, 1–10 (2016)CrossRefGoogle Scholar
  16. 16.
    Kunin, SB., Kanze, D.M.: Care for the health care provider. Med. Clin. North Am. 100, (2), 279–288 (2016)Google Scholar
  17. 17.
    Madankar, M., Chandak, M.B., Chavhan, N.: Information retrieval system and machine translation: a review. Proced. Comput. Sci. 78, 845–850 (2016)CrossRefGoogle Scholar
  18. 18.
    Losada, D.E., Parapar, J., Barreiro, A.: Multi-armed bandits for adjudicating documents in pooling-based evaluation of information retrieval systems. Inf. Process. Manag. 53(5), 1005–1025 (2017)CrossRefGoogle Scholar
  19. 19.
    Marrara, S., Pasi, G., Viviani, M.: Aggregation operators in information retrieval. Fuzzy Sets Syst. 324, 3–19 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Guo, Y., Hu, J., Peng, Y.: Research on CBR system based on data mining. Appl. Soft Comput. 11(8), 5006–5014 (2011)CrossRefGoogle Scholar
  21. 21.
    Park, Y.-J., Choi, E., Park, S.-H.: Two-step filtering datamining method integrating CBR and rule induction. Exp. Syst. Appl. 36(1), 861–871 (2009)CrossRefGoogle Scholar
  22. 22.
    Ahn, H., Kim, K.-J.: Global optimization of CBR for breast cytology diagnosis. Exp. Syst. Appl. 36(1), 724–734 (2009)CrossRefGoogle Scholar
  23. 23.
    Pandey, B., Mishra, R.: CBR and data mining integrated method for the diagnosis of some neuromuscular disease. Int. J. Med. Eng. Inf. 3(1), 1–15 (2011)Google Scholar
  24. 24.
    Chuang, C.-L.: CBR support for liver disease diagnosis. Artif. Intell. Med. 53(1), 15–23 (2011)CrossRefGoogle Scholar
  25. 25.
    Huang, M.-J., Chen, M.-Y., Lee, S.-C.: Integrating data mining with CBR for chronic diseases prognosis and diagnosis. Exp. Syst. Appl. 32(3), 856–867 (2007)CrossRefGoogle Scholar
  26. 26.
    Jun, B., Choi, I., Kim, D.: Local transform features and hybridization for accurate face and human detection. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1423–1436 (2013)CrossRefGoogle Scholar
  27. 27.
    Kunttu, I., Lepisto, L., Visa, A.: Image correlogram in image database indexing and retrieval. In: Proceedings of 4th European Workshop on Image Analysis for Multimedia Interactive Services Queen Mary, University of London (2003)Google Scholar
  28. 28.
    Akansu, A.N., Serdijn, W.A., Selesnick, I.W.: Emerging applications of wavelets: a review. Phys. Commun. 3(1), 1–18 (2010)CrossRefGoogle Scholar
  29. 29.
    Mandal, M.K., Aboulnasr, T., Panchanathan, S.: Image indexing using moments and wavelets. IEEE Trans. Consum. Electron. 42(3), 557–565 (1996)CrossRefGoogle Scholar
  30. 30.
    Glenn, T.C., Zare, A., Gader, P.D.: Bayesian fuzzy clustering. IEEE Trans. Fuzzy Syst. 23(5), 1545–1561 (2015)CrossRefGoogle Scholar
  31. 31.
    Yadav, P.: Case retrieval algorithm using similarity measure and adaptive fractional brain storm optimization for health informaticians. Arabian J. Sci. Eng. 41(3), 829–840 (2016)CrossRefGoogle Scholar
  32. 32.
    Solteiro Pires, E.J., Tenreiro Machado, J.A., de Moura Oliveira, P.B., Boaventura Cunha, J., Mendes, L.: Particle swarm optimization with fractional-order velocity. Nonlinear Dyn. 61, (1–2), 295–301 (2010)Google Scholar
  33. 33.
    Xue, J., Wu, Y., Shi, Y., Cheng, S.: Brain storm optimization algorithm for multi-objective optimization problems. In: Proceedings of the Third international conference on Advances in Swarm Intelligence, vol. I, pp. 513–519. Shenzhen, China (2012)Google Scholar
  34. 34.
    Chander, S., Vijaya, P., Dhyani, P.: Multi kernel and dynamic fractional lion optimization algorithm for data clustering. Alex. Eng. J. (2017)Google Scholar
  35. 35.
  36. 36.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.D.A.V. College of Engineering & TechnologyKaninaIndia

Personalised recommendations