Abstract
In this paper we present the first Content-Based Image Retrieval (CBIR) framework in the field of in vivo endomicroscopy, with applications ranging from training support to diagnosis support. We propose to adjust the standard Bag-of-Visual-Words method for the retrieval of endomicroscopic videos. Retrieval performance is evaluated both indirectly from a classification point-of-view, and directly with respect to a perceived similarity ground truth. The proposed method significantly outperforms, on two different endomicroscopy databases, several state-of-the-art methods in CBIR. With the aim of building a self-training simulator, we use retrieval results to estimate the interpretation difficulty experienced by the endoscopists. Finally, by incorporating clinical knowledge about perceived similarity and endomicroscopy semantics, we are able: 1) to learn an adequate visual similarity distance and 2) to build visual-word-based semantic signatures that extract, from low-level visual features, a higher-level clinical knowledge expressed in the endoscopist own language.
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André, B., Vercauteren, T., Ayache, N. (2012). Content-Based Retrieval in Endomicroscopy: Toward an Efficient Smart Atlas for Clinical Diagnosis. 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_2
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DOI: https://doi.org/10.1007/978-3-642-28460-1_2
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