Abstract
In this paper we propose a novel learning–based CBIR method for fast content–based retrieval of similar 3D images based on the intrinsic Random Forest (RF) similarity. Furthermore, we allow the combination of flexible user–defined semantics (in the form of retrieval contexts and high–level concepts) and appearance–based (low–level) features in order to yield search results that are both meaningful to the user and relevant in the given clinical case. Due to the complexity and clinical relevance of the domain, we have chosen to apply the framework to the retrieval of similar 3D CT hepatic pathologies, where search results based solely on similarity of low–level features would be rarely clinically meaningful. The impact of high–level concepts on the quality and relevance of the retrieval results has been measured and is discussed for three different set–ups. A comparison study with the commonly used canonical Euclidean distance is presented and discussed as well.
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Costa, M.J. et al. (2012). A Discriminative Distance Learning–Based CBIR Framework for Characterization of Indeterminate Liver Lesions. In: Müller, H., Greenspan, H., Syeda-Mahmood, T. (eds) Medical Content-Based Retrieval for Clinical Decision Support. MCBR-CDS 2011. Lecture Notes in Computer Science, vol 7075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28460-1_9
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DOI: https://doi.org/10.1007/978-3-642-28460-1_9
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