@InProceedings{6952664,
  author = {S. S. Kar and S. P. Maity and C. Delpha},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Retinal blood vessel extraction using curvelet transform and conditional fuzzy entropy},
  year = {2014},
  pages = {1821-1825},
  abstract = {This work employs multiple thresholds on matched filter response for automatic extraction of blood vessels, specially from a low contrast and non-uniformly illuminated background of retina. Curvelet transform is used first to enhance the finest details along the vessels followed by matched filtering to intensify the blood vessels' response. The conditional fuzzy entropy is then maximized to find the set of optimal thresholds to extract different types of vessel silhouettes from the background. Differential Evolution algorithm is used to specify the optimal combination of the fuzzy parameters. Thresholds thus obtained extract the thin, the medium and the thick vessels from the enhanced image which are then logically OR-ed to obtain the entire vascular tree. Performance evaluated on publicly available DRIVE database is compared with the existing blood vessel extraction methods. Experimental runs demonstrate that the proposed method outperforms the existing methods in detecting various types of vessels.},
  keywords = {blood vessels;curvelet transforms;entropy;evolutionary computation;feature extraction;fuzzy set theory;image enhancement;matched filters;medical image processing;retinal recognition;DRIVE database;vascular tree;logically OR-ed;image enhancement;fuzzy parameters;differential evolution algorithm;vessel silhouettes;nonuniform illuminated background;low contrast illuminated background;matched filter response;multiple thresholds;conditional fuzzy entropy;curvelet transform;automatic retinal blood vessel extraction method;Biomedical imaging;Retina;Blood vessels;Entropy;Image edge detection;Transforms;Image segmentation;Retinal vessel segmentation;Curvelet;Matched filter;Conditional Fuzzy Entropy;Differential Evolution},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569925099.pdf},
}

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Curvelet transform is used first to enhance the finest details along the vessels followed by matched filtering to intensify the blood vessels' response. The conditional fuzzy entropy is then maximized to find the set of optimal thresholds to extract different types of vessel silhouettes from the background. Differential Evolution algorithm is used to specify the optimal combination of the fuzzy parameters. Thresholds thus obtained extract the thin, the medium and the thick vessels from the enhanced image which are then logically OR-ed to obtain the entire vascular tree. Performance evaluated on publicly available DRIVE database is compared with the existing blood vessel extraction methods. Experimental runs demonstrate that the proposed method outperforms the existing methods in detecting various types of vessels.","keywords":"blood vessels;curvelet transforms;entropy;evolutionary computation;feature extraction;fuzzy set theory;image enhancement;matched filters;medical image processing;retinal recognition;DRIVE database;vascular tree;logically OR-ed;image enhancement;fuzzy parameters;differential evolution algorithm;vessel silhouettes;nonuniform illuminated background;low contrast illuminated background;matched filter response;multiple thresholds;conditional fuzzy entropy;curvelet transform;automatic retinal blood vessel extraction method;Biomedical imaging;Retina;Blood vessels;Entropy;Image edge detection;Transforms;Image segmentation;Retinal vessel segmentation;Curvelet;Matched filter;Conditional Fuzzy Entropy;Differential Evolution","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569925099.pdf","bibtex":"@InProceedings{6952664,\n author = {S. S. Kar and S. P. Maity and C. Delpha},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {Retinal blood vessel extraction using curvelet transform and conditional fuzzy entropy},\n year = {2014},\n pages = {1821-1825},\n abstract = {This work employs multiple thresholds on matched filter response for automatic extraction of blood vessels, specially from a low contrast and non-uniformly illuminated background of retina. Curvelet transform is used first to enhance the finest details along the vessels followed by matched filtering to intensify the blood vessels' response. The conditional fuzzy entropy is then maximized to find the set of optimal thresholds to extract different types of vessel silhouettes from the background. Differential Evolution algorithm is used to specify the optimal combination of the fuzzy parameters. Thresholds thus obtained extract the thin, the medium and the thick vessels from the enhanced image which are then logically OR-ed to obtain the entire vascular tree. Performance evaluated on publicly available DRIVE database is compared with the existing blood vessel extraction methods. Experimental runs demonstrate that the proposed method outperforms the existing methods in detecting various types of vessels.},\n keywords = {blood vessels;curvelet transforms;entropy;evolutionary computation;feature extraction;fuzzy set theory;image enhancement;matched filters;medical image processing;retinal recognition;DRIVE database;vascular tree;logically OR-ed;image enhancement;fuzzy parameters;differential evolution algorithm;vessel silhouettes;nonuniform illuminated background;low contrast illuminated background;matched filter response;multiple thresholds;conditional fuzzy entropy;curvelet transform;automatic retinal blood vessel extraction method;Biomedical imaging;Retina;Blood vessels;Entropy;Image edge detection;Transforms;Image segmentation;Retinal vessel segmentation;Curvelet;Matched filter;Conditional Fuzzy Entropy;Differential Evolution},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569925099.pdf},\n}\n\n","author_short":["Kar, S. 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