Abstract
Modality is a key facet in medical image retrieval, as a user is likely interested in only one of e.g. radiology images, flowcharts, and pathology photos. While assessing image modality is trivial for humans, reliable automatic methods are required to deal with large un-annotated image bases, such as figures taken from the millions of scientific publications. We present a multi-disciplinary approach to tackle the classification problem by combining image features, meta-data, textual and referential information. We test our system’s accuracy on the Image- CLEF 2011 medical modality classification data set. We show that using a fully affine-invariant feature descriptor and sparse coding on these descriptors in the Bag-of-Words image representation significantly increases the classification accuracy. Our best method achieves 87.89 and outperforms the state of the art.
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Hersh, W.R., Müller, H., Jensen, J.R., Yang, J., Gorman, P.N., Ruch, P.: Advancing Biomedical Image Retrieval: Development and Analysis of a Test Collection. Journal of the American Medical Informatics Association 13(5), 488–496 (2006)
Pentland, A., Picard, R.W., Sclaroff, S.: Photobook: Content-based manipulation of image databases. International Journal of Computer Vision 18(3), 233–254 (1996)
Lakdashti, A., Moin, M.S.: A New Content-Based Image Retrieval Approach Based on Pattern Orientation Histogram. In: Gagalowicz, A., Philips, W. (eds.) MIRAGE 2007. LNCS, vol. 4418, pp. 587–595. Springer, Heidelberg (2007)
Jain, A.: Image retrieval using color and shape. Pattern Recognition 29(8), 1233–1244 (1996)
Morel, J.-M., Yu, G.: ASIFT: A New Framework for Fully Affine Invariant Image Comparison. SIAM Journal on Imaging Sciences 2(2) (April 2009)
Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, vol. 1, p. 22 (2004)
Jarrett, K., Kavukcuoglu, K., Ranzato, M.A., LeCun, Y.: What is the best multi-stage architecture for object recognition? In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2146–2153 (2009)
Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1794–1801 (2009)
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least Angle Regression. ArXiv Mathematics e-prints (June 2004)
Kalpathy-Cramer, J., Müller, H., Bedrick, S., Eggel, I., de Herrera, A.G.S., Tsikrika, T.: The CLEF 2011 medical image retrieval and classification tasks. In: CLEF 2011 Working Notes, Amsterdam, The Netherlands (2011)
Veltkamp, R.C.: A survey of content-based image retrieval systems. Content-based Image and Video Retrieval (2002)
Duda, R.O.: Use of the Hough transformation to detect lines and curves in pictures. Communications of the ACM (1972)
Chai, D., Ngan, K.N.: Face segmentation using skin-color map in videophone applications. IEEE Transactions on Circuits and Systems for Video Technology 9(4), 551–564 (1999)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the International Conference on Computer Vision, ICCV 1999, pp. 1150–1157. IEEE Computer Society, Washington, DC (1999)
Nister, D., Stewenius, H.: Scalable Recognition with a Vocabulary Tree. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2161–2168 (2006)
Jegou, H., Harzallah, H., Schmid, C.: A contextual dissimilarity measure for accurate and efficient image search. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2007), pp. 1–8 (2007)
Chum, O., Philbin, J., Sivic, J., Isard, M.: Total Recall: Automatic query expansion with a generative feature model for object retrieval. In: 2007 IEEE 11th International Conference on Computer Vision, pp. 1–8. IEEE (October 2007)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Lost in quantization: Improving particular object retrieval in large scale image databases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2008)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Gal, V., Solt, I., Gedeon, T., Nachtegael, M., Kerre, E.: Multi-disciplinary modality classication for medical images. In: CLEF 2011 Working Notes, Amsterdam, The Netherlands (2011)
Sivic, J., Zisserman, A.: Video Google: A Text Retrieval Approach to Object Matching in Videos. In: 9th IEEE International Conference on Computer Vision (ICCV 2003), pp. 1470–1477. IEEE Computer Society (2003)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Lost in quantization: Improving particular object retrieval in large scale image databases. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (2008)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explorations 11(1), 10–18 (2009)
Csurka, G., Clinchant, S., Jacquet, G.: XRCE’s Participation at Medical Image Modality Classification and Ad-hoc Retrieval Tasks of Image CLEF 2011. In: CLEF 2011 Working Notes, Amsterdam, The Netherlands (2011)
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Gál, V., Solt, I., Kerre, E., Nachtegael, M. (2013). Modality Classification for Medical Images Using Sparse Coded Affine-Invariant Descriptors. In: Washio, T., Luo, J. (eds) Emerging Trends in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36778-6_1
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DOI: https://doi.org/10.1007/978-3-642-36778-6_1
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