Open Access
Subscription Access
Open Access
Subscription Access
Mechanism for Diabetic Retinal Blood Vessel Profile Measurement and Analysis on Fundus Images
Subscribe/Renew Journal
Diabetic Retinopathy (DR) occurs due to Type II diabetes. At the early stage, if it is identified one can save their vision. Later stage, retinal detachment leads to 100% vision loss. An automatic computer based system is needed for diagnosis. There are diverse tools and automatic and semi-automatic systems are available. But the system is not identifying and measuring the narrow blood vessels accurately, because of the noise and imaging problems. Also, while tracking the retinal vessel, the narrow vessels are equally taken into consideration as wider vessels. Thus the proposed segmentation and classification techniques extract the blood vessels and measure the profile features of fundus images obtained from dissimilar modalities significantly.
Keywords
Diabetic Retinopathy, Retinal Blood Vessel, Vessel Profile, Ophthalmology, Fundus Image.
Subscription
Login to verify subscription
User
Font Size
Information
- R. Keerthikanth, A. Rajan, Detection of Retinal Blood Vessels using Curve let Transform, ICSEC, May 2014, Vol. 2, Issue 3.
- Argyrios Christodoulidis, Thomas Hurtut, Houssem Ben Tahar, Farida Cheriet, A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images, Computerized Medical Imaging and Graphics, 2016, 28–43.
- Priyanka Yadao, Sayali Naval, Animesh Maheshwari, Blood Vessel Segmentation in Retinal Fundus Images using Matched Filter, Journal of Harmonized Research in Engineering, 2014.
- Abdolhossein Fathi, Ahmad Reza Naghsh-Nilchi1, Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation, Biomedical Signal Processing and Control, 2013, 71–80.
- Luo Gang, Opas Chutatape, Shankar M. Krishnan, Detection and Measurement of Retinal Vessels in Fundus Images Using Amplitude Modified Second- Order Gaussian Filter, IEEE Transactions on Biomedical Engineering, February 2002, Vol. 49.
- Rebekka Heitmar, Angelos A. Kalitzeos, Reliability of retinal vessel caliber measurements using a retinal oximeter, Heitmar and Kalitzeos, BMC Ophthalmology, 2015,15:184.
- Behdad Dashtbozorg, Ana Maria Mendonça, Aurelio Campilho, An Automatic Graph-Based Approach For Artery/Vein Classification In Retinal Images, IEEE Transactions On Image Processing, March 2014, Vol. 23, No. 3.
- Retinal Vessel Centerline Extraction Using Multiscale Matched Filters, Confidence and Edge Measures Michal Sofka, Charles V. Stewart, IEEE Transactions on Medical Imaging, December 2006, Vol. 25, No. 12.
- Bashir Al-Diri, Andrew Hunter, David Steel, Maged Habib, Manual Measurement of Retinal Bifurcation Features, 32nd Annual International Conference of the IEEE EMBS Buenos Aires, Argentina, August 31 - September 4, 2010.
- Bashir Al-Diri, Andrew Hunter, David Steel, An Active Contour Model for Segmenting and Measuring Retinal Vessels, IEEE Transactions on Medical Imaging, September 2009, Vol.28, No. 9.
- Andrew Hunter, David Steel, Ansu Basu, Robert Ryder, and R. Lee Kennedy, Measurement of Retinal Vessel Widths From Fundus Images Based on 2-D Modelling James Lowell, IEEE Transactions On Medical Imaging, October 2004, Vol. 23, No. 10.
- Rothaus, K, Jiang, X, Rhiem P, Separation of the retinal vascular graph in arteries and veins based upon structural knowledge, Image and Vision Computing, 2009, 864–875.
- Dashtbozorg B, Mendonca A. M, Campilho A, An automatic graph-based approach for artery/vein classification in retinal images, IEEE Transactions on Image Processing, 2013.
- Owen C. G, Rudnicka A. R, Mullen R, Barman S.A, Monekosso D, Whincup P. H, Measuring retinal vessel tortuosity in 10-yearold children: validation of the computer-assisted image analysis of the retina (CAIAR) program, Investigative Ophthalmology & Visual Science, 2004–2010.
- Ortega M, Barreira N, Novo J, Penedo M. G, Pose-Reino A, Gomez-Ulla F, Sirius: A web-based system for retinal image analysis. International Journal of Medical Informatics, 2010, 722– 732.
- Grisan E, Ruggeri A, A divide et impera strategy for automatic classification of retinal vessels into arteries and veins. In Engineering in medicine and biology society, 2003. Proceedings of the 25th annual international conference of the IEEE, 2003, Vol. 1, pp. 890–893.
- Perez-Rovira A, MacGillivray T, Trucco E, Chin K. S, Zutis K, Lupascu C, Tegolo D, Giachetti A, Wilson P. J, Doney A, Dhillon B, VAMPIRE: Vessel assessment and measurement platform for images of the Retina. Engineering in medicine and biology society, EMBC, 2011 annual international conference of the IEEE, 2011, pp. 3391–3394.
- Vazquez S. G, Cancela B, Barreira N, Penedo M. G, RodriguezBlanco M, Pena Seijo M, Improving retinal artery and vein classification by means of a minimal path approach. Machine Vision and Applications, 2013, 24, 919–930.
- Kelvin P, Ghassan H, Rafeef A, Live-vessel: extending livewire for simultaneous extraction of optimal medial and boundary paths in vascular images. In Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention. Brisbane, Australia: Springer- Verlag, 2007.
- DRIVE: http://www.isi.uu.nl/Research/Databases/DRIVE, STARE: http://cecas.clemson.edu/~ahoover/stare/, REVIEW: http://reviewdb.lincoln.ac.uk/Image%20Datasets/Review.aspx, HRF: https://www5.cs.fau.de/research/data/fundus-images/, DRIONS: http://www.ia.uned.es/~ejcarmona/DRIONS-DB.html.
Abstract Views: 276
PDF Views: 0