@InProceedings{6952744,
  author = {I. Soares and M. Castelo-Branco and A. M. G. Pinheiro},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Vessel centerline detection in retinal images based on a corner detector and dynamic thresholding},
  year = {2014},
  pages = {2020-2024},
  abstract = {This paper describes a new method for the calculation of the retinal vessel centerlines using a scale-space approach for an increased reliability and effectiveness. The algorithm begins with a new vessel detector description method based on a modified corner detector. Then the vessel detector image is filtered with a set of binary rotating filters, resulting in enhanced vessels structures. The main vessels can be selected with a dynamic thresholding approach. In order to deal with vessels bifurcations and vessels crossovers that might not be detected, the initial retinal image is processed with a set of four directional differential operators. The resulting directional images are then combined with the detected vessels, creating the final vessels centerlines image. The performance of the algorithm is evaluated using two different datasets.},
  keywords = {retinal recognition;vessel centerline detection;retinal images;dynamic thresholding;scale-space approach;vessel detector description method;modified corner detector;vessel detector image;binary rotating filters;enhanced vessel structure;dynamic thresholding approach;vessel bifurcations;vessel crossovers;directional differential operators;directional images;Image segmentation;Biomedical imaging;Detectors;Retinal vessels;Kernel;Bifurcation;Vessel centerline;scale-space;Retina},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569926009.pdf},
}

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The algorithm begins with a new vessel detector description method based on a modified corner detector. Then the vessel detector image is filtered with a set of binary rotating filters, resulting in enhanced vessels structures. The main vessels can be selected with a dynamic thresholding approach. In order to deal with vessels bifurcations and vessels crossovers that might not be detected, the initial retinal image is processed with a set of four directional differential operators. The resulting directional images are then combined with the detected vessels, creating the final vessels centerlines image. The performance of the algorithm is evaluated using two different datasets.","keywords":"retinal recognition;vessel centerline detection;retinal images;dynamic thresholding;scale-space approach;vessel detector description method;modified corner detector;vessel detector image;binary rotating filters;enhanced vessel structure;dynamic thresholding approach;vessel bifurcations;vessel crossovers;directional differential operators;directional images;Image segmentation;Biomedical imaging;Detectors;Retinal vessels;Kernel;Bifurcation;Vessel centerline;scale-space;Retina","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569926009.pdf","bibtex":"@InProceedings{6952744,\n author = {I. Soares and M. Castelo-Branco and A. M. G. Pinheiro},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {Vessel centerline detection in retinal images based on a corner detector and dynamic thresholding},\n year = {2014},\n pages = {2020-2024},\n abstract = {This paper describes a new method for the calculation of the retinal vessel centerlines using a scale-space approach for an increased reliability and effectiveness. The algorithm begins with a new vessel detector description method based on a modified corner detector. Then the vessel detector image is filtered with a set of binary rotating filters, resulting in enhanced vessels structures. The main vessels can be selected with a dynamic thresholding approach. In order to deal with vessels bifurcations and vessels crossovers that might not be detected, the initial retinal image is processed with a set of four directional differential operators. The resulting directional images are then combined with the detected vessels, creating the final vessels centerlines image. The performance of the algorithm is evaluated using two different datasets.},\n keywords = {retinal recognition;vessel centerline detection;retinal images;dynamic thresholding;scale-space approach;vessel detector description method;modified corner detector;vessel detector image;binary rotating filters;enhanced vessel structure;dynamic thresholding approach;vessel bifurcations;vessel crossovers;directional differential operators;directional images;Image segmentation;Biomedical imaging;Detectors;Retinal vessels;Kernel;Bifurcation;Vessel centerline;scale-space;Retina},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569926009.pdf},\n}\n\n","author_short":["Soares, I.","Castelo-Branco, M.","Pinheiro, A. M. 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