Abstract
In this paper, we propose a symbolic approach for classification of medical imaging modalities. Texture, appearance, and signal features are extracted from medical images. We propose to represent the extracted features by an interval valued feature vector. Unlike the conventional methods, the interval valued feature vector representation is able to preserve the variations existing among the extracted features of medical images. Based on the proposed symbolic representation, we present a method of classifying medical imaging modalities. The proposed classification method makes use of a symbolic similarity measure for classification. Experimentation is carried out on a benchmark medical imaging modalities database. Our proposed approach achieves classification within negligible time as it is based on a simple matching scheme.
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Rajaei, A., Dallalzadeh, E., Rangarajan, L. (2012). Symbolic Classification of Medical Imaging Modalities. In: Kim, Th., Ko, Ds., Vasilakos, T., Stoica, A., Abawajy, J. (eds) Computer Applications for Communication, Networking, and Digital Contents. FGCN 2012. Communications in Computer and Information Science, vol 350. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35594-3_43
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DOI: https://doi.org/10.1007/978-3-642-35594-3_43
Publisher Name: Springer, Berlin, Heidelberg
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