• DocumentCode
    2607628
  • Title

    A New Approach to Automated Retinal Vessel Segmentation Using Multiscale Analysis

  • Author

    Qin Li ; You, Jie ; Lei Zhang ; Zhang, Dejing ; Bhattacharya, Pallab

  • Author_Institution
    Dept. of Comput., Hong Kong Polytech. Univ.
  • Volume
    4
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    77
  • Lastpage
    80
  • Abstract
    Computer based analysis for automated segmentation of blood vessels in retinal images help eye care specialists screen larger populations for vessel abnormalities. However, automated retinal segmentation is complicated by the fact that the width of retinal vessels can vary from very large to very small, and that the local contrast of vessels is unstable, especially in unhealthy ocular fundus. We propose a novel method that takes these facts into account. Our method includes a multiscale analytical scheme using Gabor filters and scale production, and a threshold probing technique utilizing the features of retinal vessel network. Our method is good for detecting large and small vessels concurrently. It also offers an efficient way to denoise and enhance the responses of line filters, allowing the detection of vessels with low local contrast
  • Keywords
    Gabor filters; biology computing; blood vessels; diseases; eye; image denoising; image segmentation; Gabor filter; automated blood vessel segmentation; automated retinal segmentation; automated retinal vessel segmentation; computer based analysis; eye care specialist; multiscale analysis; multiscale analytical scheme; retinal image; retinal vessel abnormality; retinal vessel network; scale production; threshold probing technique; vessel detection; Biomedical imaging; Blood vessels; Gabor filters; Image analysis; Image edge detection; Image segmentation; Information analysis; Production; Retina; Retinal vessels;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1051-4651
  • Print_ISBN
    0-7695-2521-0
  • Type

    conf

  • DOI
    10.1109/ICPR.2006.112
  • Filename
    1699787