Multimedia Tools and Applications

, Volume 73, Issue 3, pp 1963–1981 | Cite as

A noisy-smoothing relevance feedback method for content-based medical image retrieval

  • Yonggang HuangEmail author
  • Heyan Huang
  • Jun Zhang


In this paper, we address a new problem of noisy images which present in the procedure of relevance feedback for medical image retrieval. We concentrate on the noisy images, caused by the users mislabeling some irrelevant images as relevant ones, and a noisy-smoothing relevance feedback (NS-RF) method is proposed. In NS-RF, a two-step strategy is proposed to handle the noisy images. In step 1, a noisy elimination algorithm is adopted to identify and eliminate the noisy images. In step 2, to further alleviate the influence of noisy images, a fuzzy membership function is employed to estimate the relevance probabilities of retained relevant images. After noisy handling, the fuzzy support vector machine, which can take into account different relevant images with different relevance probabilities, is adopted to re-rank the images. The experimental results on the IRMA medical image collection demonstrate that the proposed method can deal with the noisy images effectively.


CBIR Relevance feedback Noisy elimination Fuzzy membership function Noisy-smoothing 



The authors thank courtesy of TM Deserno, Dept. of Medical Informatics, RWTH Aachen, Germany, for providing IRMA dataset. This work is supported by the National Natural Science Foundation of China (No. 61300077), the Research Fund for the Doctoral Program of Higher Education of China (Query and Annotation Translation Using Visual Information for Cross-Language Image Retrieval), and the Basic Research Foundation of Beijing Institute of Technology (No. 20120742009).


  1. 1.
    Arevalillo-Herráez M, Ferri FJ, Domingo J (2010) A naive relevance feedback model for content-based image retrieval using multiple similarity measures. Pattern Recogn 43(3):619–629CrossRefzbMATHGoogle Scholar
  2. 2.
    Belkhatir M, Mulhem P, Chiaramella Y (2005) A conceptual image retrieval architecture combining keyword-based querying with transparent and penetrable query-by-example. In: Proceedings of the 4th international conference on image and video retrieval. Singapore, pp 528–539Google Scholar
  3. 3.
    Chang SF, Sikora T, Purl A (2011) Overview of the MPEG-7 standard. IEEE Trans Circuits Syst Video Technol 11(6):688–695CrossRefGoogle Scholar
  4. 4.
    Cox IJ, Miller ML, Minka TP, Papathomas TV, Yianilos PN (2002) The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments. IEEE Trans Image Process 9(1):20–37CrossRefGoogle Scholar
  5. 5.
    Crucianu M, Estevez D, Oria V, Tarel J-P (2008) Speeding up active relevance feedback with approximate knn retrieval for hyperplane queries. Int J Imaging Syst Technol 18(2–3):150–159CrossRefGoogle Scholar
  6. 6.
    Deselaers T, Paredes R, Vidal E, Ney H (2008) Learning weighted distances for relevance feedback in image retrieval. In: Proceeding of the 19th international conference on pattern recognition. Florida, pp 1–4Google Scholar
  7. 7.
    Ferreira CD, Santos JA, Torres R da S, Gonçalves MA, Rezende RC, Fan W (2011) Relevance feedback based on genetic programming for image retrieval. Pattern Recogn Lett 32(1):27–37CrossRefGoogle Scholar
  8. 8.
    Giacinto G, Roli F (2008) Instance-based relevance feedback in image retrieval using dissimilarity spaces. Case-Based Reasoning on Images and Signals 73(1):419–436CrossRefGoogle Scholar
  9. 9.
    Greenspan H, Pinhas AT (2007) Medical image categorization and retrieval for pacs using the GMM-KL framework. IEEE Trans Inf Technol Biomed 11(2):190–202CrossRefGoogle Scholar
  10. 10.
    Hoi SCH, Jin R, Zhu J, Lyu MR (2009) Semisupervised SVM batch mode active learning with applications to image retrieval. ACM Trans Inf Syst 27(3):1–29CrossRefGoogle Scholar
  11. 11.
    Iakovidis DK, Pelekis N, Kotsifakos EE, Kopanakis I, Karanikas H, Theodoridis Y (2009) A pattern similarity scheme for medical image retrieval. IEEE Trans Inf Technol Biomed 13(4):442–450CrossRefGoogle Scholar
  12. 12.
    Ishikawa Y, Subramanya R, Faloutsos C (1998) Mindreader: querying databases through multiple examples. In: Proceedings of the 24rd international conference on very large data bases. New York, pp 218–227Google Scholar
  13. 13.
    Koenemann J, Belkin NJ (1996) A case for interaction: a study of interactive information retrieval behavior and effectiveness. In: Proceedings of the SIGCHI conference on human factors in computing systems. British Columbia, Canada, pp 205–212Google Scholar
  14. 14.
    Lehmann TM, Schubert H, Keysers D, Kohnen M, Wein BB (2003) The IRMA code for unique classification of medical images. In: Proceedings of SPIE 2003, vol 5033, pp 440–451Google Scholar
  15. 15.
    Lehmann TM, Schubert H, Ott B, Leisten M (2012) Image retrieval in medical applications. RWTH Aachen University. Accessed 15 Sept 2012
  16. 16.
    Lin CF, Wang SD (2002) Fuzzy support vector machines. IEEE Trans Neural Netw 13(2):464–471CrossRefGoogle Scholar
  17. 17.
    Liu R, Wang Y, Baba T, Masumoto D, Nagata S (2008) SVM-based active feedback in image retrieval using clustering and unlabeled data. Pattern Recogn 41(8):2645–2655CrossRefzbMATHGoogle Scholar
  18. 18.
    Lu, Y, Hu C, Zhu X, Zhang HJ, Yang Q (2000) A unified framework for semantics and feature based relevance feedback in image retrieval systems. In: Proceedings of the 8th ACM international conference on multimedia. Los Angeles, pp 31–37Google Scholar
  19. 19.
    Mildenberger P, Eichelberg M, Martin E (2002) Introduction to the DICOM standard. Eur Radiol 12(4):920–927CrossRefGoogle Scholar
  20. 20.
    Müller H, Müller W, Squire DMG, Marchand-Maillet S, Pun T (2001) Performance evaluation in content-based image retrieval: overview and proposals. Pattern Recogn Lett 22(5):593–601CrossRefzbMATHGoogle Scholar
  21. 21.
    Müller H, Müller W, Marchand-Maillet S, Pun T, Squire DMG (2002) Strategies for positive and negative relevance feedback in image retrieval. In: Proceedings of the 15th international conference on pattern recognition. Barcelona, pp 1043–1046Google Scholar
  22. 22.
    Rahman MM, Bhattacharya P, Desai BC (2007) A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback. IEEE Trans Inf Technol Biomed 11(1):58–69CrossRefGoogle Scholar
  23. 23.
    Rahman MM, Desai BC, Bhattacharya P (2008) Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion. Comput Med Imaging Graph 32(2):95–108CrossRefGoogle Scholar
  24. 24.
    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):640–646CrossRefGoogle Scholar
  25. 25.
    Rahman M, Antani SK, Thoma GR (2011) A framework based on classification-driven image filtering and similarity fusion. In: IEEE international symposium on biomedical imaging: from nano to macro. Chicago, IL, USA, pp 1905–1908Google Scholar
  26. 26.
    Rao Y, Mundur P, Yesha Y (2006) Fuzzy svm ensembles for relevance feedback in image retrieval. In: Proceedings of the 5th international conference on image and video retrieval. Tempe, AZ, USA, pp 350–359Google Scholar
  27. 27.
    Rui Y, Huang TS, Ortega M, Mehrotra S (1998) Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Trans Circuits Syst Video Technol 8(5):644–655CrossRefGoogle Scholar
  28. 28.
    Salton G (1971) The SMART retrieval system: experiments in automatic document processing. Prentice HallGoogle Scholar
  29. 29.
    Scott G, Shyu CR (2007) Knowledge-driven multidimensional indexing structure for biomedical media database retrieval. IEEE Trans Inf Technol Biomed 11(3):320–331CrossRefGoogle Scholar
  30. 30.
    Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380CrossRefGoogle Scholar
  31. 31.
    Su J-H, Huang W-J, Yu PS, Tseng VS (2011) Efficient relevance feedback for content-based image retrieval by mining user navigation patterns. IEEE Trans Knowl Data Eng 23(3):360–372CrossRefGoogle Scholar
  32. 32.
    Su Z, Zhang H, Li S, Ma S (2003) Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning. IEEE Trans Image Process 12(8):924–937CrossRefGoogle Scholar
  33. 33.
    Tamura H, Mori S, Yamawaki T (1978) Textural features corresponding to visual perception. IEEE Trans Syst Man Cybern 8(6):460–473CrossRefGoogle Scholar
  34. 34.
    Tong S, Chang E (2001) Support vector machine active learning for image retrieval. In: Proceedings of the 9th ACM international conference on multimedia. Ottawa, pp 107–118Google Scholar
  35. 35.
    Wang X-F, Chen X-S (2012) Efficient image retrieval using support vector machines and bayesian relevance feedback. In: The 5th international congress on image and signal processing. Chongqing, China, pp 786–789Google Scholar
  36. 36.
    Wang X-Y, Zhang B-B, Yang H-Y (2013) Active svm-based relevance feedback using multiple classifiers ensemble and features reweighting. Eng Appl Artif Intell 26(1):368–381CrossRefMathSciNetGoogle Scholar
  37. 37.
    Xu X, Lee DJ, Antani S, Long (2008) A spine x-ray image retrieval system using partial shape matching. IEEE Trans Inf Technol Biomed 12(1):100–108CrossRefGoogle Scholar
  38. 38.
    Zhang L, Lin F, Zhang B (2001) Support vector machine learning for image retrieval. In: Proceedings of the 18th international conference on image processing. Thessaloniki, pp 721–724Google Scholar
  39. 39.
    Zhao L, Tang J, Yu X, Li Y, Mi S, Zhang C (2012) Content-based remote sensing image retrieval using image multi-feature combination and svm-based relevance feedback. Recent Advances in Computer Science and Information Engineering 124:761–767CrossRefGoogle Scholar
  40. 40.
    Zhou XS, Huang TS (2003) Relevance feedback in image retrieval: a comprehensive review. Multimedia Systems 8(6):536–544CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Beijing Engineering Research Center of High Volume Language Information Processing & Cloud Computing ApplicationsBeijing Institute of TechnologyBeijingChina
  2. 2.School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingChina
  3. 3.School of Information TechnologyDeakin UniversityVictoriaAustralia

Personalised recommendations