Retinal Blood Vessel Segmentation with Neural Network by Using Gray-Level Co-Occurrence Matrix-Based Features-Reference-Cited by-同舟云学术

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Retinal Blood Vessel Segmentation with Neural Network by Using Gray-Level Co-Occurrence Matrix-Based Features

Published:2014-06-24 Issue:8 Volume:38 Page:
ISSN:0148-5598
Container-title:Journal of Medical Systems
language:en
Short-container-title:J Med Syst

Author:

Rahebi Javad,Hardalaç Fırat

Publisher

Springer Science and Business Media LLC

Subject

Health Information Management,Health Informatics,Information Systems,Medicine (miscellaneous)

Link

http://link.springer.com/content/pdf/10.1007/s10916-014-0085-2.pdf

Reference15 articles.

1. Staal, J., Abr’amoff, M. D., Niemeijer, M., Viergever, M. A., and van Ginneken, B., “Ridgebased vessel segmentation in color images of the retina”. IEEE Trans on Medical Imaging 23(4):501–509, 2004.

2. Marín, D., Aquino, A., Gegúndez-Arias, M. E., and Bravo, J. M., A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imag. 30(1):146–158, 2011.

3. Ricci, E., and Perfetti, R., Retinal blood vessel segmentation using line operators and support vector classification. IEEE Transactions on Medical Imaging 26(10):1357–1365, 2007.

4. Hoover, A., Kouznetsova, V., and Goldbaum, M., Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3):203–210, 2000.

5. Thomos, N., Boulgouris, N. V., and Strintzis, M. G., Optimized transmission of JPEG2000 streams over wireless channels. IEEE Trans on Image Processing 15(1):54–67, 2006.

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