An Efficient Medical Content Based Image Retrieval with Lenient Relevance Feedback

  • Authors

    • R I. Heaven Rose
    • A C. Subajini
    2018-07-04
    https://doi.org/10.14419/ijet.v7i3.6.14965
  • CBIR, CBMIR, CAD, association rule, euclidean distance, query vector modification, relevance association rule mining, hard feedback, lenient feedback.

  • Abstract

    Content Based Image Retrieval (CBIR) for medical imageries is still in its early stage.  There are many challenging research issues.  Retrieve similar images only is the current problem in medical CBIR. One idea to solve this difficult is minimizing the gap among two descriptions i.e. low level extracted features of image and high level human perception of image.  There are various Relevance Feedback (WF) methods have been considered to minimize the semantic gap in medical CBIR system. But most of them were deals with hard Feedback. In Hard Feedback system user can interact with the system in one query session. We recommend to aid the usage of lenient relevance response to better capture the intention of users. The meta-knowledge mined from multiple user’s experience be able to   increase the precision of subsequent image recovery results. Here we suggest an algorithm to mine lenient association rules from the group of suggestion i.e. image weight value given by the user. To reduce the amount of strong rules we offer two rule lessening techniques related to redundancy detection and confidence quantization.  Best first search and Binary search methods are similarly applied to advance the procedure of weight interface. The effectiveness of the offered system is assessed regarding precision and average retrieval time. The experimental results on medical images display that the proposed method is able to improve the accuracy of medical CBIR system and reduces the retrieval time than other usual methods.

     

     

  • References

    1. [1] Lin TW, Huang PY & Cheng CWC, “Computer-aided diagnosis in medical imaging: Review of legal barriers to entry for the commercial systemsâ€, IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), (2016), pp.1-5.[2] Raghu K & Swarup M, “Content-Based Image Retrieval Based on a Fuzzy Approachâ€, IEEE Transactions on Knowledge and Data Engineering, Vol.16, No.10, (2004).[3] Prachi B & Cheeran AN, “Content Based Image Retrieval in Medical Imagingâ€, International Journal of Computational Engineering Research, Vol.3 No.8, (2013).

      [4] Sunitha J & Sihem M, “Towards Effective Relevance Feedback Methods in Content-Based Image Retrieval Systemsâ€, International Journal of Innovation, Management and Technology, Vol.5, No.1, (2012), pp. 201435-38.

      [5] Rajalakshmi T & Minu RI, “Improving relevance feedback for content based medical image retrievalâ€, IEEE International Conference on Information Communication and Embedded Systems (ICICES), (2014), pp.1-5.

      [6] Patil PB & Kokare MB, “Content Based Image Retrieval with Relevance Feedback using Riemannian Manifoldsâ€, IEEE Fifth International Conference on Signal and Image Processing (ICSIP), (2014), pp.26-29.

      [7] Aswini KM & Manas RS, “A Novel Image Mining Technique for Classification of Mammograms Using Hybrid Feature Selectionâ€, International Journal of Advanced Research in Computer Science, Vol.2, No.3, (2011).

      [8] Bugatti PR, “Content-Based Retrieval of Medical Images by Continuous Feature Selectionâ€, 21st IEEE International Symposium on Computer-Based Medical Systems, (2008).

      [9] Baddeti S & Yarravarapu SR, “An Effective Similarity Measure via Genetic Algorithms for Content Based Image Retrieval with Extensive Featuresâ€, Multimedia Tools Applications, Vol.17, No.17, (2013).

      [10] Durga Rajesh KV & Mukesh D, “Squared Euclidean Distance Matrix: A Heuristic Based Approach for Cell Formation Applicationsâ€, International Journal of Applied Engineering Research, Vol.11, No.10, (2016), pp.7044-7048.

      [11] Liang L, Jun P & Bo Y, “Image Feature Selection Based on Genetic Algorithmâ€, Proceedings of the International Conference on Information Engineering and Applications (IEA) (2012).

      [12] Rabeb M, Mohamed T & Hawete H, “A New Relevance Feedback Algorithm Based on Vector Space Basis Changeâ€, International Conference on Intelligent Text Processing and Computational Linguistics, (2014).

      [13] Hossein A & Abolfazl L, “A Feature Relevance Estimation Method for Content-Based Image Retrievalâ€, International Journal of Information Technology & Decision Making, Vol.10, No.5, (2011).

      [14] Tatiana AC & Pavel PC, “Image Retrieval Using Multiple Evidence Rankingâ€, IEEE Transactions on Knowledge and Data Engineering, Vol.16, No.4, (2004).

      [15] Begüm D and Lorenzo B, “Fellow, A Novel Active Learning Method in Relevance Feedback for Content-Based Remote Sensing Image Retrievalâ€, IEEE Transactions On Geoscience And Remote Sensing, Vol.53, No.5, (2015).

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  • How to Cite

    I. Heaven Rose, R., & C. Subajini, A. (2018). An Efficient Medical Content Based Image Retrieval with Lenient Relevance Feedback. International Journal of Engineering & Technology, 7(3.6), 175-178. https://doi.org/10.14419/ijet.v7i3.6.14965

    Received date: 2018-07-02

    Accepted date: 2018-07-02

    Published date: 2018-07-04