Abstract
Global features describe the image content by a small number of numerical values, which are usually combined into a vector of less than 1,024 components. Since color is not present in most medical images, grey-scale and texture features are analyzed in order to distinguish medical imagery from various modalities. The reference data is collected arbitrarily from radiological routine. Therefore, all anatomical regions and biological systems are present and all images have been captured in various directions. The ground truth is established by manually reference coding with respect to a mono-hierarchical unambiguous coding scheme. Based on 6,335 images, experiments are performed for 54 and 57 categories or 70 and 81 categories focusing on radiographs only or considering all images, respectively. A maximum classification accuracy of 86% was obtained using the winner-takes-all rule and a one nearest neighbor classifier. If the correct category is only required to be within the 5 or 10 best matches, we yield a best rate of 98% using normalized cross correlation of small image icons.
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Lehmann, T.M. et al. (2004). Similarity of Medical Images Computed from Global Feature Vectors for Content-Based Retrieval. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30133-2_131
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DOI: https://doi.org/10.1007/978-3-540-30133-2_131
Publisher Name: Springer, Berlin, Heidelberg
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