Abstract
Modality is a key facet in medical image retrieval, as a user is likely interested in only one of e.g. radiology images, flowcharts, and pathology photos. While assessing image modality is trivial for humans, reliable automatic methods are required to deal with large un-annotated image bases, such as figures taken from the millions of scientific publications. We present a multi-disciplinary approach to tackle the classification problem by combining image features, meta-data, textual and referential information. We test our system’s accuracy on the Image- CLEF 2011 medical modality classification data set. We show that using a fully affine-invariant feature descriptor and sparse coding on these descriptors in the Bag-of-Words image representation significantly increases the classification accuracy. Our best method achieves 87.89 and outperforms the state of the art.
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Gál, V., Solt, I., Kerre, E., Nachtegael, M. (2013). Modality Classification for Medical Images Using Sparse Coded Affine-Invariant Descriptors. In: Washio, T., Luo, J. (eds) Emerging Trends in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36778-6_1
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DOI: https://doi.org/10.1007/978-3-642-36778-6_1
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