Introducing space and time in local feature-based endomicroscopic image retrieval

Barbara André, Tom Vercauteren, Aymeric Perchant, Anna M. Buchner, Michael B. Wallace, Nicholas Ayache

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

Interpreting endomicroscopic images is still a significant challenge, especially since one single still image may not always contain enough information to make a robust diagnosis. To aid the physicians, we investigated some local feature-based retrieval methods that provide, given a query image, similar annotated images from a database of endomicroscopic images combined with high-level diagnosis represented as textual information. Local feature-based methods may be limited by the small field of view (FOV) of endomicroscopy and the fact that they do not take into account the spatial relationship between the local features, and the time relationship between successive images of the video sequences. To extract discriminative information over the entire image field, our proposed method collects local features in a dense manner instead of using a standard salient region detector. After the retrieval process, we introduce a verification step driven by the textual information in the database and in which spatial relationship between the local features is used. A spatial criterion is built from the co-occurence matrix of local features and used to remove outliers by thresholding on this criterion. To overcome the small-FOV problem and take advantage of the video sequence, we propose to combine image retrieval and mosaicing. Mosaicing essentially projects the temporal dimension onto a large field of view image. In this framework, videos, represented by mosaics, and single images can be retrieved with the same tools. With a leave-n-out cross-validation, our results show that taking into account the spatial relationship between local features and the temporal information of endomicroscopic videos by image mosaicing improves the retrieval accuracy.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages18-30
Number of pages13
Volume5853 LNCS
DOIs
StatePublished - 2010
Event1st MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CDS 2009 - London, United Kingdom
Duration: Sep 20 2009Sep 20 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5853 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CDS 2009
CountryUnited Kingdom
CityLondon
Period9/20/099/20/09

Fingerprint

Local Features
Image retrieval
Image Retrieval
Detectors
Field of View
Retrieval
Thresholding
Cross-validation
Outlier
Detector
Entire
Query
Relationships

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

André, B., Vercauteren, T., Perchant, A., Buchner, A. M., Wallace, M. B., & Ayache, N. (2010). Introducing space and time in local feature-based endomicroscopic image retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5853 LNCS, pp. 18-30). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5853 LNCS). https://doi.org/10.1007/978-3-642-11769-5_2

Introducing space and time in local feature-based endomicroscopic image retrieval. / André, Barbara; Vercauteren, Tom; Perchant, Aymeric; Buchner, Anna M.; Wallace, Michael B.; Ayache, Nicholas.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5853 LNCS 2010. p. 18-30 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5853 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

André, B, Vercauteren, T, Perchant, A, Buchner, AM, Wallace, MB & Ayache, N 2010, Introducing space and time in local feature-based endomicroscopic image retrieval. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 5853 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5853 LNCS, pp. 18-30, 1st MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CDS 2009, London, United Kingdom, 9/20/09. https://doi.org/10.1007/978-3-642-11769-5_2
André B, Vercauteren T, Perchant A, Buchner AM, Wallace MB, Ayache N. Introducing space and time in local feature-based endomicroscopic image retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5853 LNCS. 2010. p. 18-30. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-11769-5_2
André, Barbara ; Vercauteren, Tom ; Perchant, Aymeric ; Buchner, Anna M. ; Wallace, Michael B. ; Ayache, Nicholas. / Introducing space and time in local feature-based endomicroscopic image retrieval. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5853 LNCS 2010. pp. 18-30 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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