Segmenting the eye fundus images for identification of blood vessels
Retinal (eye fundus) images are widely used for diagnostic purposes by ophthalmologists. The normal features of eye fundus images include the optic nerve disc, fovea and blood vessels. Algorithms for identifying blood vessels in the eye fundus image generally fall into two classes: extraction of ves...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Vilnius Gediminas Technical University
2012-02-01
|
| Series: | Mathematical Modelling and Analysis |
| Subjects: | |
| Online Access: | https://journals.vgtu.lt/index.php/MMA/article/view/4816 |
| _version_ | 1818387661498351616 |
|---|---|
| author | Gediminas Balkys Gintautas Dzemyda |
| author_facet | Gediminas Balkys Gintautas Dzemyda |
| author_sort | Gediminas Balkys |
| collection | DOAJ |
| description | Retinal (eye fundus) images are widely used for diagnostic purposes by ophthalmologists. The normal features of eye fundus images include the optic nerve disc, fovea and blood vessels. Algorithms for identifying blood vessels in the eye fundus image generally fall into two classes: extraction of vessel information and segmentation of vessel pixels. Algorithms of the first group start on known vessel point and trace the vasculature structure in the image. Algorithms of the second group perform a binary classification (vessel or non-vessel, i.e. background) in accordance of some threshold. We focus here on the binarization [4] methods that adapt the threshold value on each pixel to the global/local image characteristics. Global binarization methods [5] try to find a single threshold value for the whole image. Local binarization methods [3] compute thresholds individually for each pixel using information from the local neighborhood of the pixel. In this paper, we modify and improve the Sauvola local binarization method [3] by extending its abilities to be applied for eye fundus pictures analysis. This method has been adopted for automatic detection of blood vessels in retinal images. We suggest automatic parameter selection for Sauvola method. Our modification allows determine/extract the blood vessels almost independently of the brightness of the picture. |
| first_indexed | 2024-12-14T04:13:30Z |
| format | Article |
| id | doaj.art-7cb151eda5e34052989139938a4b87ba |
| institution | Directory Open Access Journal |
| issn | 1392-6292 1648-3510 |
| language | English |
| last_indexed | 2024-12-14T04:13:30Z |
| publishDate | 2012-02-01 |
| publisher | Vilnius Gediminas Technical University |
| record_format | Article |
| series | Mathematical Modelling and Analysis |
| spelling | doaj.art-7cb151eda5e34052989139938a4b87ba2022-12-21T23:17:36ZengVilnius Gediminas Technical UniversityMathematical Modelling and Analysis1392-62921648-35102012-02-0117110.3846/13926292.2012.644046Segmenting the eye fundus images for identification of blood vesselsGediminas Balkys0Gintautas Dzemyda1Institute of Mathematics and Informatics, Vilnius University Akademijos 4, LT-08663 Vilnius, LithuaniaInstitute of Mathematics and Informatics, Vilnius University Akademijos 4, LT-08663 Vilnius, LithuaniaRetinal (eye fundus) images are widely used for diagnostic purposes by ophthalmologists. The normal features of eye fundus images include the optic nerve disc, fovea and blood vessels. Algorithms for identifying blood vessels in the eye fundus image generally fall into two classes: extraction of vessel information and segmentation of vessel pixels. Algorithms of the first group start on known vessel point and trace the vasculature structure in the image. Algorithms of the second group perform a binary classification (vessel or non-vessel, i.e. background) in accordance of some threshold. We focus here on the binarization [4] methods that adapt the threshold value on each pixel to the global/local image characteristics. Global binarization methods [5] try to find a single threshold value for the whole image. Local binarization methods [3] compute thresholds individually for each pixel using information from the local neighborhood of the pixel. In this paper, we modify and improve the Sauvola local binarization method [3] by extending its abilities to be applied for eye fundus pictures analysis. This method has been adopted for automatic detection of blood vessels in retinal images. We suggest automatic parameter selection for Sauvola method. Our modification allows determine/extract the blood vessels almost independently of the brightness of the picture.https://journals.vgtu.lt/index.php/MMA/article/view/4816image analysisbinarizationretinal imageseye fundusblood vessels identification |
| spellingShingle | Gediminas Balkys Gintautas Dzemyda Segmenting the eye fundus images for identification of blood vessels Mathematical Modelling and Analysis image analysis binarization retinal images eye fundus blood vessels identification |
| title | Segmenting the eye fundus images for identification of blood vessels |
| title_full | Segmenting the eye fundus images for identification of blood vessels |
| title_fullStr | Segmenting the eye fundus images for identification of blood vessels |
| title_full_unstemmed | Segmenting the eye fundus images for identification of blood vessels |
| title_short | Segmenting the eye fundus images for identification of blood vessels |
| title_sort | segmenting the eye fundus images for identification of blood vessels |
| topic | image analysis binarization retinal images eye fundus blood vessels identification |
| url | https://journals.vgtu.lt/index.php/MMA/article/view/4816 |
| work_keys_str_mv | AT gediminasbalkys segmentingtheeyefundusimagesforidentificationofbloodvessels AT gintautasdzemyda segmentingtheeyefundusimagesforidentificationofbloodvessels |