Endomicroscopic image retrieval and classification using invariant visual features

Authors:
B. André;T. Vercauteren;A. Perchant;A. M. Buchner;M. B. Wallace;N. Ayache
Affiliations:
Mauna Kea Technologies, Paris and INRIA, Sophia-Antipolis, France;Mauna Kea Technologies, Paris, France;Mauna Kea Technologies, Paris, France;Mayo Clinic, Jacksonville, Florida;Mayo Clinic, Jacksonville, Florida;INRIA, Sophia-Antipolis, France
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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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.