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Proceedings Article

Automatic quantitative evaluation of image registration techniques with the ε dissimilarity criterion in the case of retinal images

[+] Author Affiliations
Yann Gavet, Mathieu Fernandes, Jean-Charles Pinoli

CIS-LPMG, CNRS, Ecole Nationale Supérieure des Mines de Saint-Etienne (France)

Proc. SPIE 8000, Tenth International Conference on Quality Control by Artificial Vision, 80000H (July 12, 2011); doi:10.1117/12.890883
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From Conference Volume 8000

  • Tenth International Conference on Quality Control by Artificial Vision
  • Jean-Charles Pinoli; Johan Debayle; Yann Gavet; Frédéric Gruy; Claude Lambert
  • Saint-Etienne, France | June 28, 2011

abstract

In human retina observation (with non mydriatic optical microscopes), a registration process is often employed to enlarge the field of view. For the ophthalmologist, this is a way to spare time browsing all the images. A lot of techniques have been proposed to perform this registration process, and indeed, its good evaluation is a question that can be raised. This article presents the use of the ε dissimilarity criterion to evaluate and compare some classical featurebased image registration techniques. The problem of retina images registration is employed as an example, but it could also be used in other applications. The images are first segmented and these segmentations are registered. The good quality of this registration is evaluated with the dissimilarity criterion for 25 pairs of images with a manual selection of control points. This study can be useful in order to choose the type of registration method and to evaluate the results of a new one.

© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Yann Gavet ; Mathieu Fernandes and Jean-Charles Pinoli
"Automatic quantitative evaluation of image registration techniques with the ε dissimilarity criterion in the case of retinal images", Proc. SPIE 8000, Tenth International Conference on Quality Control by Artificial Vision, 80000H (July 12, 2011); doi:10.1117/12.890883; http://dx.doi.org/10.1117/12.890883


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