IDEAS home Printed from https://ideas.repec.org/a/igg/jhisi0/v12y2017i1p14-29.html
   My bibliography  Save this article

Automatic Detection of Blood Vessel in Retinal Images Using Vesselness Enhancement Filter and Adaptive Thresholding

Author

Listed:
  • Abderrahmane Elbalaoui

    (Sultan Moulay Slimane University, Beni Mellal, Morocco)

  • Mohamed Fakir

    (Faculty of Science and Technology, Sultan Moulay Slimane University, Beni Mellal, Morocco)

  • Taifi khaddouj

    (Sultan Moulay Slimane University, Beni Mellal, Morocco)

  • Abdelkarim MERBOUHA

    (Sultan Moulay Slimane University, Beni Mellal, Morocco)

Abstract

Retinal blood vessels detection and measurement of morphological attributes, such as length, width, sinuosity and corners are very much important for the diagnosis and treatment of different ocular diseases including diabetic retinopathy (DR), glaucoma, and hypertension. This paper presents a integration method for blood vessels detection in fundus retinal images. The proposed method consists of two main steps. The first step is pre-processing of retinal image to improve the retinal images by evaluation of several image enhancement techniques. The second step is vessels detection, the vesselness filter is usually used to enhance the blood vessels. The enhancement filter is designed from the adaptive thresholding of the output of the vesselness filter for vessels detection. The algorithms performance is compared and analyzed on three publicly available databases (DRIVE, STARE and CHASE_DB) of retinal images using a number of measures, which include accuracy, sensitivity, and specificity.

Suggested Citation

  • Abderrahmane Elbalaoui & Mohamed Fakir & Taifi khaddouj & Abdelkarim MERBOUHA, 2017. "Automatic Detection of Blood Vessel in Retinal Images Using Vesselness Enhancement Filter and Adaptive Thresholding," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 12(1), pages 14-29, January.
    • Handle: RePEc:igg:jhisi0:v:12:y:2017:i:1:p:14-29
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJHISI.2017010102
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jhisi0:v:12:y:2017:i:1:p:14-29. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.