Abstract
Real-life clinical cases are important sources for computer diagnosis and pathologic analysis. In this paper, we propose a novel medical case retrieval framework based on multi-graph semi-supervised learning. The presented framework aims to retrieve multi-modality medical cases consisting of images together with diagnostic information. In particular, we first introduce a multi-graph semi-supervised learning method, which unifies both visual and textual information during learning by minimizing the cost function on a fusion graph. Then, a manifold ranking scheme is generated based on this multi-graph structure for retrieval. Experiments on the LIDC dataset and the mammographic patches dataset validate the effectiveness of the proposed method.
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
Preview
Unable to display preview. Download preview PDF.
References
Müller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A Review of Content-Based Image Retrieval Systems in Medical Applications-Clinical Benefits and Future Directions. Int. J. Med. Inform. 73, 1–23 (2004)
Wei, C.H., Li, Y., Li, C.T.: Effective Extraction of Gabor Features for Adaptive Mammogram Retrieval. In: IEEE International Conference on Multimedia and Expo 2007, pp. 1503–1506. IEEE Press (2007)
Xu, X., Lee, D.J., Antani, S., Long, L.R.: A Spine X-Ray Image Retrieval System Using Partial Shape Matching. IEEE. T. Inf. Technol. B. 12, 100–108 (2008)
Avni, U., Greenspan, H., Konen, E., Sharon, M.: X-Ray Categorization and Retrieval on the Organ and Pathology Level, Using Patch-Based Visual Words. IEEE. T. Med. Imaging 30, 733–746 (2011)
Yang, L., Jin, R., Mummert, L., Sukthankar, R.: A Boosting Framework for Visuality-Preserving Distance Metric Learning and Its Application to Medical Image Retrieval. IEEE. T. Pattern. Anal. 32, 30–44 (2010)
Rahman, M.M., Antani, S.K., Thoma, G.R.: A Learning-Based Similarity Fusion and Filtering Approach for Biomedical Image Retrieval Using SVM Classification and Relevance Feedback. IEEE. T. Inf. Technol. B. 15, 640–646 (2011)
Shao, H., Cui, W.C., Zhao, H.: Medical Image Retrieval Based on Visual Contents and Text Information. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 1098–1103. IEEE Press (2004)
Quellec, G., Lamard, M., Bekri, L.: Medical Case Retrieval From A Committee ofDecision Trees. IEEE. T. Inf. Technol. B. 14, 1227–1235 (2010)
Quellec, G., Lamard, M., Cazuguel, G.: Case Retrieval in Medical Databases by Fusing Heterogeneous Information. IEEE. T. Med. Imaging 30, 108–118 (2011)
Tsevas, S., Iakovidis, D.K.: Dynamic Time Warping Fusion for The Retrieval ofSimilar Patient Cases Represented by Multimodal Time-Series Medical Data. In: IEEE International Conference on Information Technology and Applications in Biomedicine, pp. 1–4. IEEE Press (2010)
Ko, B.C., Lee, J., Nam, J.: Automatic Medical Image Annotation and Keyword-Based Image Retrieval Using Relevance Feedback. J. Digit. Imaging 25, 454–465 (2012)
Kumar, A., Kim, J., Wen, L.: A Graph-Based Approach for The Retrieval ofMulti-Modality Medical Images. Med. Image. Anal. 18, 330–342 (2013)
Burdescu, D., Mihai, G.C., Stanescu, L.: Automatic Image Annotation and Semantic Based Image Retrieval for Medical Domain. Neurocomputing 109, 33–48 (2013)
Lacoste, C., Lim, J., Chevallet, J., Le, D.T.H.: Medical-Image Retrieval Based on Knowledge-Assisted Text and Image Indexing. IEEE. T. Circ. Syst. Vid. 17, 889–900 (2007)
Zhu, X.: Semi-Supervised Learning with Graphs, Ph.D. Dissertation, Carnegie Mellon Univ., Pittsburgh, PA (2005)
Subramanya, A., Bilmes, J.: Semi-Supervised Learning with Measure Propagation. J. Mach. Learn. Res. 12, 3311–3370 (2011)
Tang, J.H., Li, H.J., Qi, G.: Image Annotation by Graph-Based Inference with Integrated Multiple/Single Instance Representations. IEEE. T. Multimedia 12, 131–141 (2010)
He, J.R., Li, M.J., Zhang, H.J.: Generalized Manifold-Ranking-Based Image Retrieval. IEEE. T. Image. Process. 15, 3170–3177 (2006)
Lee, W.Y., Hsieh, L., Wu, G., Hsu, W.: Graph-Based Semi-Supervised Learning with Multi-Modality Propagation for Large-Scale Image Datasets. J. Vis. Commun. Image. R. 24, 295–302 (2013)
Muhammad, M.N., Raicu, D.S., Furst, G.D., Varutbangkul, E.: Texture Versus Shape Analysis for Lung Nodule Similarity in Computed Tomography Studies. In: Proceedings of the SPIE, vol. 6919 (2008)
Oliveira, J., Machado, A., Chavez, G., Lopes, A., Deserno, T.M., AraÚJo, A.: Mammosys: A Content-Based Image Retrieval System Using Breast Density Patterns. Comput. Meth. Prog. Bio. 99, 289–297 (2010)
Sun, Q.S., Zheng, S.G., Liu, Y.: A New Method of Feature Fusion and Its Application in Image Recognition. Pattern Recogn. 38, 2437–2448 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Wu, M., Sun, Q. (2014). Multi-modality Medical Case Retrieval Using Heterogeneous Information. In: Huang, DS., Han, K., Gromiha, M. (eds) Intelligent Computing in Bioinformatics. ICIC 2014. Lecture Notes in Computer Science(), vol 8590. Springer, Cham. https://doi.org/10.1007/978-3-319-09330-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-09330-7_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09329-1
Online ISBN: 978-3-319-09330-7
eBook Packages: Computer ScienceComputer Science (R0)