@inproceedings{fc03c5074592457685055827e11dcf57,
title = "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.",
keywords = "Bag of Visual Words (BVW) method, Content-based imageretrieval, Endomicroscopy, K-nearest neighbors classification",
author = "B. Andr{\'e} and T. Vercauteren and A. Perchant and Buchner, {A. M.} and Wallace, {M. B.} and N. Ayache",
year = "2009",
doi = "10.1109/ISBI.2009.5193055",
language = "English (US)",
isbn = "9781424439324",
series = "Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009",
pages = "346--349",
booktitle = "Proceedings - 2009 IEEE International Symposium on Biomedical Imaging",
note = "2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 ; Conference date: 28-06-2009 Through 01-07-2009",
}