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.
Similar content being viewed by others
References
Khansa, L., Davis, Z., Davis, H., Chin, A., MacMichael, N.: Health information technologies for patients with diabetes. Technol. Soc. 4, 1–94 (2016)
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)
Depeursinge, A., Duc, S., Eggel, I., Müller, H.: Mobile medical visual information retrieval. IEEE Trans. Inf. Technol. Biomed. 16, (1), 53–61 (2012)
Lisa, L.M.: Ethics and subsequent use of electronic health record data. J. Biomed. Inf. 71, 143–146 (2017)
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)
Zhang, J., Xu, W., Guo, J., Gao, S.: A temporal model in electronic health record search. Knowl.-Based Syst. 126, 56–67 (2017)
Spil, T.A.M., Cellucci, L.W.: Electronic health records across the nations. Health Policy Technol. 4(2), 89–90 (2015)
Penrod, L.E.: Electronic health record transition considerations. PM&R 9(5), s13–s18 (2017)
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)
Qayyum, A., Anwar, S.M., Awais, M., Majid, M.: Medical image retrieval using deep convolutional neural network. Neurocomputing 266, 8–20 (2017)
Piras, L., Giacinto, G.: Information fusion in content-based image retrieval: a comprehensive overview. Inf. Fusion 37, 50–60 (2017)
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)
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)
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)
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)
Kunin, SB., Kanze, D.M.: Care for the health care provider. Med. Clin. North Am. 100, (2), 279–288 (2016)
Madankar, M., Chandak, M.B., Chavhan, N.: Information retrieval system and machine translation: a review. Proced. Comput. Sci. 78, 845–850 (2016)
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)
Marrara, S., Pasi, G., Viviani, M.: Aggregation operators in information retrieval. Fuzzy Sets Syst. 324, 3–19 (2017)
Guo, Y., Hu, J., Peng, Y.: Research on CBR system based on data mining. Appl. Soft Comput. 11(8), 5006–5014 (2011)
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)
Ahn, H., Kim, K.-J.: Global optimization of CBR for breast cytology diagnosis. Exp. Syst. Appl. 36(1), 724–734 (2009)
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)
Chuang, C.-L.: CBR support for liver disease diagnosis. Artif. Intell. Med. 53(1), 15–23 (2011)
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)
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)
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)
Akansu, A.N., Serdijn, W.A., Selesnick, I.W.: Emerging applications of wavelets: a review. Phys. Commun. 3(1), 1–18 (2010)
Mandal, M.K., Aboulnasr, T., Panchanathan, S.: Image indexing using moments and wavelets. IEEE Trans. Consum. Electron. 42(3), 557–565 (1996)
Glenn, T.C., Zare, A., Gader, P.D.: Bayesian fuzzy clustering. IEEE Trans. Fuzzy Syst. 23(5), 1545–1561 (2015)
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)
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)
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)
Chander, S., Vijaya, P., Dhyani, P.: Multi kernel and dynamic fractional lion optimization algorithm for data clustering. Alex. Eng. J. (2017)
DIARETDB0 database from http://www.it.lut.fi/project/imageret/diaretdb0/
BRATS database from https://www.smir.ch/BRATS/Start2015
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yadav, P. Cluster based-image descriptors and fractional hybrid optimization for medical image retrieval. Cluster Comput 22 (Suppl 1), 1345–1359 (2019). https://doi.org/10.1007/s10586-017-1625-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-017-1625-6