Abstract
With the increasing of medical images that are routinely acquired in clinical practice, automatic medical image classification has become an important research topic recently. In this paper, we propose an efficient medical image classification algorithm, which works by mapping local image patches to multi-resolution histograms built both in feature space and image space and then matching sets of features though weighted histogram intersection. The matching produces a kernel function that satisfies Mercer’s condition, and a multi-class SVM classifier is then applied to classify the images. The dual-space pyramid matching scheme explores not only the distribution of local features in feature space but also their spatial layout in the images. Therefore, more accurate implicit correspondence is built between feature sets. We evaluate the proposed algorithm on the dataset for the automatic medical image annotation task of ImageCLEFmed 2005. It outperforms the best result of the campaign as well as the pyramid matchings that only perform in single space.
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References
Lehmann, T.M., Güld, M.O., Deselaers, T., Keysers, D., Schubert, H., Spitzer, K., Ney, H., Wein, B.B.: Automatic Categorization of Medical Images for Content-based Retrieval and Data Mining. Computerized Medical Imaging and Graphics 29, 143–155 (2005)
Grauman, K., Darrell, T.: The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV 2005), Beijing, China (October 2005)
Grauman, K., Darrell, T.: Approximate Correspondences in High Dimensions. MIT CSAIL Technical report, MIT-CSAIL-TR-2006-045 (June 2006)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2006), New York (June 2006)
Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Deselaers, T., Müeller, H., Clough, P., Ney, H., Lehmann, T.M.: The CLEF 2005 Automatic Medical Image Annotation Task. International Journal of Computer Vision (in press, 2006)
Keysers, D., Gollan, C., Ney, H.: Classification of Medical Images using Non-linear Distortion Models. In: Bildverarbeitung für die Medizin 2004 (BVM 2004), Berlin, Germany, March 2004, pp. 366–370 (2004)
Deselaers, T., Keysers, D., Ney, H.: Discriminative Training for Object Recognition Using Image Patches. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, CA (June 2005)
Marée, R., Geurts, P., Piater, J., Wehenkel, L.: Biomedical Image Classification with Random Subwindows and Decision Trees. In: Proceedings of ICCV workshop on Computer Vision for Biomedical Image Applications (CVIBA 2005), Beijing, China (October 2005)
Nistér, D., Stewénius, H.: Scalable Recognition with a Vocabulary Tree. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2006), New York (June 2006)
Fritzke, B.: Growing Cell Structures – A Self-Organizing Network in k Dimensions. Artificial Neural Networks II, 1051–1056 (1992)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Chang, C.-C., Lin, C.-J.: LIBSVM: A Library for Support Vector Machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
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© 2006 Springer-Verlag Berlin Heidelberg
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Hu, Y., Li, M., Li, Z., Ma, Wy. (2006). Dual-Space Pyramid Matching for Medical Image Classification. In: Cham, TJ., Cai, J., Dorai, C., Rajan, D., Chua, TS., Chia, LT. (eds) Advances in Multimedia Modeling. MMM 2007. Lecture Notes in Computer Science, vol 4351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69423-6_10
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DOI: https://doi.org/10.1007/978-3-540-69423-6_10
Publisher Name: Springer, Berlin, Heidelberg
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