April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
Automatic detection of the retinal neovascularization in diabetic patients.
Author Affiliations & Notes
  • benjamin béouche-hélias
    university of poitiers, Poitiers, France
    ophthalmology, University Hospital of Poitiers, Poitiers, France
  • David Helbert
    xlim-sic cnrs, poitiers, France
  • Bruno Mercier
    xlim-sic cnrs, poitiers, France
  • Christine Fernandez-Maloigne
    xlim-sic cnrs, poitiers, France
  • Nicolas Leveziel
    ophthalmology, University Hospital of Poitiers, Poitiers, France
  • Footnotes
    Commercial Relationships benjamin béouche-hélias, None; David Helbert, None; Bruno Mercier, None; Christine Fernandez-Maloigne, None; Nicolas Leveziel, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 207. doi:https://doi.org/
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      benjamin béouche-hélias, David Helbert, Bruno Mercier, Christine Fernandez-Maloigne, Nicolas Leveziel; Automatic detection of the retinal neovascularization in diabetic patients.. Invest. Ophthalmol. Vis. Sci. 2014;55(13):207. doi: https://doi.org/.

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

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Abstract
 
Purpose
 

Diabetes is a major pathology of which the prevalence is increasing worldwide. The retinal neovascularization related to diabetes is a major cause of blindness. Automatic evaluation of the extent of retinal neovascularization on ultra wide field imaging is actually not accurate. This study aimed to develop an automatically detection of retinal neovascularization including quantitative analyses during angiographic phases (surface and number of new vessels).

 
Methods
 

A multimodal imaging platform allowing an active eye tracking and ultra-wide field imaging of 105 degrees was used. Ten patients with retinal neovascularization observed during the angiographic phases were included in the analysis. The images have been automatically processed to detect the retinal neovascularization using an algorithm that included anisotropic smoothing, luminance analysis, region fusion, opening and automatic reframing between different phases of angiograms. Retinal neovascularization were computed and compared between images acquired during the early and the late phases of acquisition.

 
Results
 

This algorithm has been tested on 9 angiographic sequences from 5 different patients with proliferative diabetic retinopathy on the Spectralis platform of Heidelberg Engineering GmbH. When compared to manual analysis, 44 of the 50 neo vessels manually detected have been automatically detected (89.91%). 48.425 mm2 of the 51.616 mm2 (total surface of the neo vessels manually detected) have been automatically detected (92.54%). A mean of less than 1 false alarm (0.88) have been detected and for the total segmented surface (56.151 mm2).

 
Conclusions
 

This software provides surface and number of retinal new vessels analyses and will be able to calculate a diffusion index of the retinal new vessels. Development convivial version of this software will provide ophthalmologists quantitative data to follow diabetic patients with proliferative diabetic retinopathy.

 
Keywords: 549 image processing • 550 imaging/image analysis: clinical • 466 clinical (human) or epidemiologic studies: treatment/prevention assessment/controlled clinical trials  
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