Abstract
We describe our experiments for the Image CLEF medical retrieval task. Our efforts were focused on the initial visual search. A content-based approach was followed. We used texture, localisation and colour features that have been proven by previous experiments. The images in the collection had specific characteristics. Medical images have a formulaic composition for each modality and anatomic region. We were able to choose features that would perform well in this domain. Tiling a Gabor texture feature to add localisation information proved to be particularly effective. The distances from each feature were combined with equal weighting. This smoothed the performance across the queries. The retrieval results showed that this simple approach was successful, with our system coming third in the automatic retrieval task.
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References
Clough, P., Müller, H., Sanderson, M.: The CLEF Cross Language Image Retrieval Track (ImageCLEF) 2004. In: Peters, C., Clough, P., Gonzalo, J., Jones, G.J.F., Kluck, M., Magnini, B. (eds.) CLEF 2004. LNCS, vol. 3491, pp. 597–613. Springer, Heidelberg (2005)
Tieu, K., Viola, P.: Boosting image retrieval. In: International Conference on Spoken Language Processing (2000)
Haralick, R.: Statistical and structural approaches to texture. Proceedings of the IEEE 67, 786–804 (1979)
Gotlieb, C.C., Kreyszig, H.E.: Texture descriptors based on co-occurrence matrices. Computer Vision, Graphics and Image Processing 51, 70–86 (1990)
Howarth, P., Rüger, S.: Evaluation of texture features for content-based image retrieval. In: Enser, P.G.B., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 326–334. Springer, Heidelberg (2004)
Turner, M.: Texture discrimination by Gabor functions. Biological Cybernetics 55, 71–82 (1986)
Manjunath, B., Ma, W.: Texture features for browsing and retrieval of image data. IEEE Trans. on Pattern Analysis and Machine Intelligence 18, 837–842 (1996)
Manjunath, B.S., Ohm, J.R.: Color and texture descriptors. IEEE Trans. on circuits and systems for video technology 11, 703–715 (2001)
Ruiz, M., Srikanth, M.: UB at CLEF2004: Part 2 – cross language medical image retrieval. In: Peters, C., Clough, P., Gonzalo, J., Jones, G.J.F., Kluck, M., Magnini, B. (eds.) CLEF 2004. LNCS, vol. 3491, pp. 773–780. Springer, Heidelberg (2005)
Deselaers, T., Keysers, D., Ney, H.: FIRE - flexible image retrieval engine: ImageCLEF 2004 evaluation. In: Peters, C., Clough, P., Gonzalo, J., Jones, G.J.F., Kluck, M., Magnini, B. (eds.) CLEF 2004. LNCS, vol. 3491, pp. 688–698. Springer, Heidelberg (2005)
Heesch, D., Rüger, S.: NNk networks for content-based image retrieval. In: McDonald, S., Tait, J.I. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 253–266. Springer, Heidelberg (2004)
Yavlinsky, A., Pickering, M., Heesch, D., Rüger, S.: A comparative study of evidence combination strategies. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. III, pp. 1040–1043 (2004)
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Howarth, P., Yavlinsky, A., Heesch, D., Rüger, S. (2005). Medical Image Retrieval Using Texture, Locality and Colour. In: Peters, C., Clough, P., Gonzalo, J., Jones, G.J.F., Kluck, M., Magnini, B. (eds) Multilingual Information Access for Text, Speech and Images. CLEF 2004. Lecture Notes in Computer Science, vol 3491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11519645_72
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DOI: https://doi.org/10.1007/11519645_72
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