Endomicroscopic image retrieval and classification using invariant visual features

Abstract : This paper investigates the use of modern content based image retrieval methods to classify endomicroscopic images into two categories: neoplastic (pathological) and benign. We describe first the method that maps an image into a visual feature signature which is a numerical vector invariant with respect to some particular classes of geometric and intensity transformations. Then we explain how these signatures are used to retrieve from a database the k closest images to a new image. The classification is finally achieved through a procedure of votes weighted by a proximity criterion (weighted k-nearest neighbors). Compared with several previously published alternatives whose maximal accuracy rate is almost 67 % on the database, our approach yields an accuracy of 80 % and offers promising perspectives.
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Communication dans un congrès
Proceedings of the Sixth IEEE International Symposium on Biomedical Imaging 2009 (ISBI'09), 2009, Boston, MA, United States. IEEE, pp.346--349, 2009, 〈10.1109/ISBI.2009.5193055〉
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Barbara André, Tom Vercauteren, Aymeric Perchant, Anna M. Buchner, Michael B. Wallace, et al.. Endomicroscopic image retrieval and classification using invariant visual features. Proceedings of the Sixth IEEE International Symposium on Biomedical Imaging 2009 (ISBI'09), 2009, Boston, MA, United States. IEEE, pp.346--349, 2009, 〈10.1109/ISBI.2009.5193055〉. 〈inria-00616140〉

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