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    Automatic grading of retinal vessel caliber

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    88
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    Author
    Li, HQ; Hsu, W; Lee, ML; Wong, TY
    Date
    2005-07-01
    Source Title
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
    Publisher
    IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
    University of Melbourne Author/s
    Wong, Tien
    Affiliation
    Ophthalmology Eye and Ear Hospital
    Metadata
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    Document Type
    Journal Article
    Citations
    Li, H. Q., Hsu, W., Lee, M. L. & Wong, T. Y. (2005). Automatic grading of retinal vessel caliber. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 52 (7), pp.1352-1355. https://doi.org/10.1109/TBME.2005.847402.
    Access Status
    This item is currently not available from this repository
    URI
    http://hdl.handle.net/11343/29265
    DOI
    10.1109/TBME.2005.847402
    Abstract
    New clinical studies suggest that narrowing of the retinal blood vessels may be an early indicator of cardiovascular diseases. One measure to quantify the severity of retinal arteriolar narrowing is the arteriolar-to-venular diameter ratio (AVR). The manual computation of AVR is a tedious process involving repeated measurements of the diameters of all arterioles and venules in the retinal images by human graders. Consistency and reproducibility are concerns. To facilitate large-scale clinical use in the general population, it is essential to have a precise, efficient and automatic system to compute this AVR. This paper describes a new approach to obtain AVR. The starting points of vessels are detected using a matched Gaussian filter. The detected vessels are traced with the help of a combined Kalman filter and Gaussian filter. A modified Gaussian model that takes into account the central light reflection of arterioles is proposed to describe the vessel profile. The width of a vessel is obtained by data fitting. Experimental results indicate a 97.1% success rate in the identification of vessel starting points, and a 99.2% success rate in the tracking of retinal vessels. The accuracy of the AVR computation is well within the acceptable range of deviation among the human graders, with a mean relative AVR error of 4.4%. The system has interested clinical research groups worldwide and will be tested in clinical studies.
    Keywords
    Artificial Intelligence and Image Processing

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