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An efficient retrieval using edge GLCM and association rule mining guided IPSO based artificial neural network

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Abstract

Content Based Image Retrieval (CBIR) is a challenging research area due to increase in multimedia database and other image libraries day by day. With an intent to provide an efficient search and retrieval, we propose an enhanced Content Based Medical Image Retrieval (CBMIR) system to support the medical practitioners in their diagnosis task. For which, we introduce boosted feature extraction and retrieval phase for medical images using Edge GLCM (EGLCM) and Association Rule Mining (ARM) integrated with Artificial Neural Network (ANN). Improved Particle Swarm Optimization (IPSO) is deployed to optimize the weights of ANN. The system is put forth with four important phases; 1. Pre-Processing, 2. Feature Extraction using Edge Histogram Descriptor (EHD), Local Gabor XOR Pattern (LGXP) and EGLCM, 3. Association Rule Mining using Apriori and 4. Optimized Retrieval using IPSO based ANN and Euclidean distance. In ANN, 7,000 images are trained and 1,100 images are tested. On Comparison with the existing systems, our method has shown best results with improved accuracy of 95 % in addition to reduced computational complexity by pre-processing and dimensionality reduction through minimal feature vector.

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References

  1. AlGarni G, Hamiane M (2008) A novel technique for automatic shoeprint image retrieval. Forensic Sci Int 181(1):10–14

    Article  Google Scholar 

  2. Avni U, Greenspan H, Konen E, Sharon M, Goldberger J (2011) X-ray categorization and retrieval on the organ and pathology level, using patch-based visual words. IEEE Trans Med Imag 30(3):733–746

    Article  Google Scholar 

  3. Balan JAAR, Rajan SE (2014) An intelligent framework for medical image retrieval using MDCT and multi SVM. Technol Health Care 22(1):13–25

  4. Bhagat A, Atique M (2014) DICOM Image retrieval using geometric moments and fuzzy connectedness image segmentation algorithm. In ICT and critical infrastructure: proceedings of the 48th annual convention of computer society of India- I:109–116

  5. Bugatti PH, Kaster DS, Ponciano-Silva M, Jr CT, Azevedo-Marques PM, Traina AJM (2014) PRoSPer: perceptual similarity queries in medical CBIR systems through user profiles. Comput Biol Med 45:8–19

    Article  Google Scholar 

  6. Chinnasamy S (2014) Performance improvement of fuzzy-based algorithms for medical image retrieval. Image Proc IET 8(6):319–326

    Article  Google Scholar 

  7. Dass MV, Ali, MM, Ali MR (2014) Image retrieval using interactive genetic algorithm, in Computational Science and Computational Intelligence (CSCI), 2014 International Conference on, 1:215–220

  8. Fan X, Malone B, Yuan C (2014) Finding optimal Bayesian network structures with constraints learned from data. In Proceedings of the 30th Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)

  9. Fan X, Yuan C (2015) An improved lower bound for Bayesian network structure learning. In AAAI, pp 3526–3532

  10. Fan X, Yuan C, Malone BM (2014). Tightening Bounds for Bayesian Network Structure Learning. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI-14), Quebec City, Quebec. pp. 2439–2445

  11. Hafiane A, Chaudhuri S, Seetharaman G, Zavidovique B (2006) Region-based CBIR in GIS with local space filling curves to spatial representation. Pattern Recog Lett 27(4):259–267

    Article  Google Scholar 

  12. Khan YD, Ahmad F, Khan SA (2014) Content-based image retrieval using extroverted semantics: a probabilistic approach. Neural Comput Appl 24(7–8):1735–1748

    Article  Google Scholar 

  13. Kumar A, Kim J, Wen L, Fulham M, Feng D (2014) A graph-based approach for the retrieval of multi-modality medical images. Med Image Anal 18(2):330–342

    Article  Google Scholar 

  14. Kumari RSS, Kannan P (2014) FPGA implementation of rotational invariant local XOR pattern operator for face recognition system. Aust J Basic Appl Sci 8(1):165–177

    Google Scholar 

  15. Kurhe AB, Satonka SS, Khanale PB (2011) Color matching of images by using minkowski-form distance. Global J Comp Sci Technol 11(5):86–89

  16. Kurtz C, Depeursinge A, Napel S, Beaulieu CF, Rubin DL (2014) On combining image-based and ontological semantic dissimilarities for medical image retrieval applications. Med Image Anal 18(7):1082–1100

  17. Lai CH (2013) A colour image retrieval scheme based on Z-scanning technique. Imag Sci J 61(3):320–333

    Article  Google Scholar 

  18. Lai CC, Chen YC (2011) A user-oriented image retrieval system based on interactive genetic algorithm. IEEE Trans Instrum Meas 60(10):3318–3325

    Article  Google Scholar 

  19. Mathew SP, Balas VE, Zachariah KP, Samuel P (2014) A content-based image retrieval system based on polar raster edge sampling signature. Acta Polytech Hungarica 11(3):25–36

  20. Medina JM, Jaime-Castillo S, Barranco CD, Campana JR (2012) On the use of a fuzzy object-relational database for flexible retrieval of medical images. IEEE Trans Fuzz Syst 20(4):786–803

    Article  Google Scholar 

  21. Medjeded M, Mahmoudi S, Chikh M (2014) Texture and classifier based medical images retrieval. J Med Imag Heal Informat 4(1):43–48

    Article  Google Scholar 

  22. Müller H, Michoux N, Bandon D, Geissbuhler A (2004) A review of content-based image retrieval systems in medical applications - clinical benefits and future directions. Int J Med Inform 73(1):1–23

    Article  Google Scholar 

  23. Murala S, Wu QMJ (2014) MRI and CT image indexing and retrieval using local mesh peak valley edge patterns. Signal Proc Imag Commun 29(3):400–409

    Article  Google Scholar 

  24. Nandagopalan S, Adiga BS, Sudarshan TSB, Dhanalakshmi C, Manjunath CN (2012) A content-based image retrieval system for echo images using SQL-based clustering approach. Imag Sci J 60(5):256–271

    Article  Google Scholar 

  25. Rahman MM, Antani SK, Thoma GR (2011) A learning-based similarity fusion and filtering approach for biomedical image retrieval using SVM classification and relevance feedback. IEEE Trans Inf Technol Biomed 15(4):640–646

    Article  Google Scholar 

  26. Sadek S, Al-Hamadi A, Michaelis B, Sayed U (2009) Image retrieval using cubic splines neural networks. International Journal of Video & Image Processing and Network Security (IJIPNS) 9(10):17–22

  27. Schnorrenberg F, Pattichis CS, Schizas CN, Kyriacou K (2000) Content-based retrieval of breast cancer biopsy slides. Technol Heal Care 8(5):291–297

    MATH  Google Scholar 

  28. Selvi SM, Kavitha C (2014) Radiographic medical image retrieval system for both organ and pathology level using bag of visual words. Int J Eng Sci Emerg Technol 6(4):410–416

    Google Scholar 

  29. Sriramakrishnan C, Shanmugam A (2012) An fuzzy neural approach for medical image retrieval. J Comput Sci 8(11):1809

  30. Tao D, Tang X, Li X (2008) Which components are important for interactive image searching? IEEE Trans Circuit Syst Video Technol 18(1):3–11

    Article  Google Scholar 

  31. Tsai HH, Chang BM, Liou SH (2014) Rotation-invariant texture image retrieval using particle swarm optimization and support vector regression. Appl Soft Comput 17:127–139

    Article  Google Scholar 

  32. Vipparthi SK, Nagar SK (2014) Color directional local quinary patterns for content based indexing and retrieval. Human Cent Comput Inf Sci 4(1):1–13

    Article  Google Scholar 

  33. Wang XY, Li YW, Yang HY, Chen JW (2014) An image retrieval scheme with relevance feedback using feature reconstruction and SVM reclassification. Neurocomp 127:214–230

    Article  Google Scholar 

  34. Yang L, Jin R, Mummert L, Sukthankar R, Goode A, Zheng B, Hoi SCH, Satyanarayanan M (2010) 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

    Article  Google Scholar 

  35. Yang HY, Li YW, Li WY, Wang XY, Yang FY (2014) Content-based image retrieval using local visual attention feature. J Vis Commun Image Rep 25(6):1308–1323

    Article  Google Scholar 

  36. Yin PY, Li SH (2006) Content-based image retrieval using association rule mining with soft relevance feedback. J Vis Commun Image Rep 17(5):1108–1125

    Article  Google Scholar 

  37. Zhang X, Liu W, Dundar M, Badve S, Zhang S (2014) Towards large-scale histopathological image analysis: hashing-based image retrieval. IEEE Trans Med Imaging 34(2):496–506

  38. Zheng L, Wetzel AW, Gilbertson J, Becich MJ (2003) Design and analysis of a content-based pathology image retrieval system. IEEE Trans Inf Technol Biomed 7(4):249–255

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Anna University, Chennai, Tamil Nadu, India

    Thenkalvi Boomilingam

  2. Department of Computer Science & Engineering, Panimalar Engineering College, Anna University, Chennai, Tamil Nadu, India

    Murugavalli Subramaniam

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  1. Thenkalvi Boomilingam
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  2. Murugavalli Subramaniam
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Correspondence to Thenkalvi Boomilingam.

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Boomilingam, T., Subramaniam, M. An efficient retrieval using edge GLCM and association rule mining guided IPSO based artificial neural network. Multimed Tools Appl 76, 21729–21747 (2017). https://doi.org/10.1007/s11042-016-3969-y

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  • DOI: https://doi.org/10.1007/s11042-016-3969-y

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