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A fast, efficient and automated method to extract vessels from fundus images

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Abstract

We present a fast, efficient, and automatic method for extracting vessels from retinal images. The proposed method is based on the second local entropy and on the gray-level co-occurrence matrix (GLCM). The algorithm is designed to have flexibility in the definition of the blood vessel contours. Using information from the GLCM, a statistic feature is calculated to act as a threshold value. The performance of the proposed approach was evaluated in terms of its sensitivity, specificity, and accuracy. The results obtained for these metrics were 0.9648, 0.9480, and 0.9759, respectively. These results show the high performance and accuracy that the proposed method offers. Another aspect evaluated in this method is the elapsed time to carry out the segmentation. The average time required by the proposed method is 3 s for images of size 565 × 584 pixels. To assess the ability and speed of the proposed method, the experimental results are compared with those obtained using other existing methods.

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Acknowledgments

The authors of this paper wish to thank the National Polytechnic Institute, Center for Computing Research and Postgraduate and Research Secretary, Mexico, for their support under the research grant number SIP 20082213.

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Authors and Affiliations

  1. Center for Computing Research, National Polytechnic Institute, Av. Juan de Dios Batiz and Miguel Othon de Mendizabal, Mexico, D.F., 07738, Mexico

    Fabiola M. Villalobos-Castaldi, Edgardo M. Felipe-Riverón & Luis P. Sánchez-Fernández

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  1. Fabiola M. Villalobos-Castaldi
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  2. Edgardo M. Felipe-Riverón
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  3. Luis P. Sánchez-Fernández
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Correspondence to Edgardo M. Felipe-Riverón.

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Villalobos-Castaldi, F.M., Felipe-Riverón, E.M. & Sánchez-Fernández, L.P. A fast, efficient and automated method to extract vessels from fundus images. J Vis 13, 263–270 (2010). https://doi.org/10.1007/s12650-010-0037-y

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