Determination of retinal network skeleton through mathematical morphology. Morales, S., Naranjo, V., Angulo, J., López-Mir, F., & Alcañiz, M. In 2014 22nd European Signal Processing Conference (EUSIPCO), pages 1691-1695, Sep., 2014.
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Paper abstract bibtex
Paper abstract bibtex This paper describes a new approach to determine vascular skeleton in retinal images. This approach is based on mathematical morphology along with curvature evaluation. In particular, a variant of the watershed transformation, the stochastic watershed, is applied to extract the vessel center-line. Its goal is to obtain directly the skeleton of the retinal tree avoiding a previous stage of vessel segmentation in order to reduce the dependence between stages and the computational cost. Experimental results show qualitative improvements if the proposed method is compared with other state-of-the-art algorithms, above all on pathological images. Therefore, the result of this work is an efficient and effective vessel centerline extraction algorithm and can be useful for further applications and image-aided diagnosis systems.
@InProceedings{6952618,
author = {S. Morales and V. Naranjo and J. Angulo and F. López-Mir and M. Alcañiz},
booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
title = {Determination of retinal network skeleton through mathematical morphology},
year = {2014},
pages = {1691-1695},
abstract = {This paper describes a new approach to determine vascular skeleton in retinal images. This approach is based on mathematical morphology along with curvature evaluation. In particular, a variant of the watershed transformation, the stochastic watershed, is applied to extract the vessel center-line. Its goal is to obtain directly the skeleton of the retinal tree avoiding a previous stage of vessel segmentation in order to reduce the dependence between stages and the computational cost. Experimental results show qualitative improvements if the proposed method is compared with other state-of-the-art algorithms, above all on pathological images. Therefore, the result of this work is an efficient and effective vessel centerline extraction algorithm and can be useful for further applications and image-aided diagnosis systems.},
keywords = {image segmentation;mathematical morphology;medical image processing;retinal recognition;retinal network skeleton determination;mathematical morphology;retinal vascular skeleton determination;retinal images;curvature evaluation;stochastic watershed transformation;vessel segmentation;pathological images;vessel centerline extraction algorithm;image-aided diagnosis systems;Skeleton;Image segmentation;Retina;Morphology;Databases;Biomedical imaging;Diseases;Retinal vascular skeleton;vessel centerline;mathematical morphology;curvature evaluation;stochastic watershed},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569922109.pdf},
}Downloads: 0
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