Abstract
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.
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Acknowledgement
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).
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Huang, Y., Huang, H. & Zhang, J. A noisy-smoothing relevance feedback method for content-based medical image retrieval. Multimed Tools Appl 73, 1963–1981 (2014). https://doi.org/10.1007/s11042-013-1685-4
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DOI: https://doi.org/10.1007/s11042-013-1685-4