June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
A Fully Automatic Framework for Segmentation and Localization of Retinal Structures in Fundus Images
Author Affiliations & Notes
  • Attila Budai
    Pattern Recognition Lab, University of Erlangen-Nuremberg, Erlangen, Germany
    Erlangen Graduate School in Advanced Optical Technologies, Erlangen, Germany
  • Katja Mogalle
    Pattern Recognition Lab, University of Erlangen-Nuremberg, Erlangen, Germany
  • Kornélia Lenke Laurik
    Department of Ophthalmology, Semmelweis University, Budapest, Hungary
  • Joachim Hornegger
    Pattern Recognition Lab, University of Erlangen-Nuremberg, Erlangen, Germany
    Erlangen Graduate School in Advanced Optical Technologies, Erlangen, Germany
  • Georg Michelson
    Interdisciplinary Center of Ophthalmic Preventive Medicine and Imaging (IZPI), Erlangen, Germany
  • Footnotes
    Commercial Relationships Attila Budai, None; Katja Mogalle, None; Kornélia Lenke Laurik, None; Joachim Hornegger, Optovue Inc. (P) (P); Georg Michelson, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 5507. doi:
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      Attila Budai, Katja Mogalle, Kornélia Lenke Laurik, Joachim Hornegger, Georg Michelson; A Fully Automatic Framework for Segmentation and Localization of Retinal Structures in Fundus Images. Invest. Ophthalmol. Vis. Sci. 2013;54(15):5507.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract
 
Purpose
 

Segmentation and localization of retinal structures is an essential pre-processing step for many methods in automatic or computer aided medical diagnosis. In this work, we present a framework for segmenting and localizing three important retinal structures in color fundus images: vascular tree, optic nerve head, and fovea region.

 
Methods
 

First we perform a vessel segmentation to extract the vascular tree. In this method a resolution hierarchy and a vesselness feature extractor is combined with hysteresis thresholding to generate a binary vessel map. As second step the Fast Radial Symmetry Transform is applied on the fundus image. This method is designed to localize the center point of circular structures, which we use to find the optic nerve head center and estimate its diameter. The obtained vessel mask and optic nerve head information is then used to estimate the center of the fovea region in the image by fitting a double parabola onto a calculated vessel density map through the optic nerve head. This estimation is then refined by analyzing a region of interest around it. Each method in the framework is tested on the public available high resolution fundus (HRF) database (see: http://www5.cs.fau.de/research/data/fundus-images/), and the results are compared to a gold standard. This database contains 15 images each of healthy eyes, glaucomatous eyes and eyes with diabetic retinopathy. The gold standard of an image contains the following information to evaluate the automatic results: -manually labeled binary vessel images -coordinates of the center and the diameter of optic nerve head -coordinates of fovea center

 
Results
 

The vessel segmentation shows accuracy over 0.93, the optic nerve head localization has an accuracy of 0.98. The macula localization is tested on a subset of 20 images containing both healthy and pathologic images. Until the presentation of this work we will continue with the evaluation using the other 25 images. This preliminary evaluation shows a localization error under 0.15 optic nerve head diameter.

 
Conclusions
 

Each of the presented methods in this framework shows a high accuracy in our evaluation. Thus, the framework can be used effectively to aid medical diagnosis by providing segmentation and localization of important retinal structures in fundus images.

 
 
Processing pipeline of the proposed framework
 
Processing pipeline of the proposed framework
 
Keywords: 551 imaging/image analysis: non-clinical  
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