Abstract
The development of medical images acquisition and storage technology has led to the rapid growth of the relevant data. Retrieval of similar medical images can effectively help doctors to diagnose diseases more accurately. But because of the particularity of medical images, traditional content-based image retrieval (CBIR) method such as bag-of-words (BOW) cannot be applied to medical images. For example, when retrieving a diseased image, we should not only consider the similar characteristics but also need to consider the type of lesion. And for medical images, images with the same lesion may have different image features, similar images may have different types of lesions. In this paper, a Markov random field (MRF) is structured, and an approximate belief propagation algorithm is used to retrieval images. An adjust-ranking step after initial retrieval is incorporated to further improve the retrieval performance. This paper uses the real brain CT images. The experimental results show that the proposed method can significantly improve the retrieval accuracy and has good efficiency.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Müller, H., Deserno, T.M.: Content-based medical image retrieval. In: Deserno, T. (ed.) Biomedical Image Processing. Biological and Medical Physics, Biomedical Engineering, pp. 471–494. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15816-2_19
Haiwei, P., Xie, X., Wei, Z., Li, J.: Mining image sequence similarity patterns in brain images. In: Yang, Q., Webb, G. (eds.) PRICAI 2006. LNCS, vol. 4099, pp. 965–969. Springer, Heidelberg (2006). doi:10.1007/978-3-540-36668-3_115
Tian, X., Jiao, L., Liu, X., Zhang, X.: Feature integration of EODH and color-sift: application to image retrieval based on codebook. Sig. Process. Image Commun. 29(4), 530–545 (2014)
Li, Y., Zhou, C., Geng, B., Xu, C., Liu, H.: A comprehensive study on learning to rank for content-based image retrieval. Sig. Process. 93(6), 1426–1434 (2013)
Lu, P., Peng, X., Zhu, X., Li, R.: Finding more relevance: propagating similarity on Markov random field for object retrieval. Sig. Process. Image Commun. 32, 54–68 (2015)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn, pp. 396–399. China Machine Press, Beijing (2006)
Tao, R., Gavves, E., Snoek, C., Smeulders, A.: Locality in generic instance search from one example. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2099–2106 (2014)
Wan, J., Wang, D., Hoi, S.C.H., Wu, P., Zhu, J., Zhang, Y., Li, J.: Deep learning for content-based image retrieval: a comprehensive study. In: ACM Multimedia (2014)
Avrithis, Y.: Quantize and conquer: a dimensionality-recursive solution to clustering, vector quantization, and image retrieval. In: IEEE International Conference on Computer Vision (ICCV), pp. 3024–3031 (2013)
Zheng, L., Wang, S., Zhou, W., Tian, Q.: Bayes merging of multiple vocabularies for scalable image retrieval. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1963–1970 (2014)
Qin, D., Wengert, C., Van Gool, L.: Query adaptive similarity for large scale object retrieval. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1610–1617 (2013)
Yang, H., Bai, X., Zhou, J., Ren, P., Zhang, Z., Cheng, J.: Adaptive object retrieval with kernel reconstructive hashing. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1955–1962 (2014)
Arandjelovic, R., Zisserman, A.: Multiple queries for large scale specific object retrieval. In: British Machine Vision Conference (2012)
Fernando, B., Tuytelaars, T.: Mining multiple queries for image retrieval: on-the-fly learning of an object-specific mid-level representation. In: IEEE International Conference on Computer Vision (ICCV), pp. 2544–2551 (2013)
Shen, X., Lin, Z., Brandt, J., Avidan, S., Wu, Y.: Object retrieval and localization with spatially-constrained similarity measure and K-NN re-ranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3013–3020 (2012)
Chen, Y., Li, X., Dick, A., Hill, R.: Ranking consistency for image matching and object retrieval. Pattern Recogn. 47(3), 1349–1360 (2014)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)
Li, S.Z.: Markov Random Field Modeling in Image Analysis. Advances in Pattern Recognition. Springer, London (2009)
Yedidia, J.S., Freeman, W.T., Weiss, Y.: Understanding belief propagation and its generalizations. Technical report (2001)
DiMaio, F., Shavlik, J.: Belief propagation in large, highly connected graphs for 3D part-based object recognition. In: Sixth International Conference on Data Mining, ICDM 2006, pp. 845–850 (2006)
Zhang, F., Song, Y.: Dictionary pruning with visual word significance for medical image retrieval. Neurocomputing 177(C), 75–88 (2016)
Acknowledgements
The paper is supported by the National Natural Science Foundation of China under Grant Nos. 61672181 and 51679058, Natural Science Foundation of Heilongjiang Province under Grant No. F2016005.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, T., Pan, H., Xie, X., Zhang, Z., Feng, X. (2017). A New Method for Medical Image Retrieval Based on Markov Random Field. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_38
Download citation
DOI: https://doi.org/10.1007/978-981-10-6385-5_38
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6384-8
Online ISBN: 978-981-10-6385-5
eBook Packages: Computer ScienceComputer Science (R0)