Abstract
In this paper, we present an approach to classify medical imaging modalities. Medical images are preprocessed in order to remove noises and enhance their content. The features based on texture, appearance and signal are extracted. The extracted features are concatenated to each other and considered for classification. KNN and SVM classifiers are applied to classify medical imaging modalities. The proposed approach is conducted on IMageCLEF2010 dataset. We achieve classification accuracy 95.39 % that presents the efficiency of our proposed approach.
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Acknowledgments
The authors would like to thank TM Lehmann, Department of Medical Informatics, RWTH Aachen, Germany, for making the database available for the experiments.
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Rajaei, A., Dallalzadeh, E., Rangarajan, L. (2013). Classification of Medical Imaging Modalities Based on Visual and Signal Features. In: S, M., Kumar, S. (eds) Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012). Lecture Notes in Electrical Engineering, vol 222. Springer, India. https://doi.org/10.1007/978-81-322-1000-9_44
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DOI: https://doi.org/10.1007/978-81-322-1000-9_44
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