Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017:2017:4897258.
doi: 10.1155/2017/4897258. Epub 2017 Aug 3.

Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification

Zafer Yavuz  1 , Cemal Köse  1
Affiliations

Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification

Zafer Yavuz et al. J Healthc Eng. 2017.

Abstract

Retinal blood vessels have a significant role in the diagnosis and treatment of various retinal diseases such as diabetic retinopathy, glaucoma, arteriosclerosis, and hypertension. For this reason, retinal vasculature extraction is important in order to help specialists for the diagnosis and treatment of systematic diseases. In this paper, a novel approach is developed to extract retinal blood vessel network. Our method comprises four stages: (1) preprocessing stage in order to prepare dataset for segmentation; (2) an enhancement procedure including Gabor, Frangi, and Gauss filters obtained separately before a top-hat transform; (3) a hard and soft clustering stage which includes K-means and Fuzzy C-means (FCM) in order to get binary vessel map; and (4) a postprocessing step which removes falsely segmented isolated regions. The method is tested on color retinal images obtained from STARE and DRIVE databases which are available online. As a result, Gabor filter followed by K-means clustering method achieves 95.94% and 95.71% of accuracy for STARE and DRIVE databases, respectively, which are acceptable for diagnosis systems.

PubMed Disclaimer

Figures

Figure 1
Figure 1
An example of color retinal fundus image (a) and manually segmented binary vessel map (b).
Figure 2
Figure 2
The block diagram of the method.
Figure 3
Figure 3
An example of retinal region selection operation.
Figure 4
Figure 4
An example to vessel light reflex removal.
Figure 5
Figure 5
Different orientations of Gabor and Gauss kernels used in vessel enhancement.
Figure 6
Figure 6
Example results of different filters (Gabor, Gaussian, and Frangi) and binary result.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Chaudhuri S., Chatterjee S., Katz N., Nelson M., Goldbaum M. Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Transactions on Medical Imaging. 1989;8(3):263–269. doi: 10.1109/42.34715. - DOI - PubMed
    1. Hoover A. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging. 2000;19(3):203–210. doi: 10.1109/42.845178. - DOI - PubMed
    1. Walter T., Klein J.-C. Segmentation of color fundus images of the human retina: detection of the optic disc and the vascular tree using morphological techniques. Medical Data Analysis. 2001. pp. 282–287.
    1. Zana F., Klein J. C. Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Transactions on Image Processing. 2001;10(7):1010–1019. doi: 10.1109/83.931095. - DOI - PubMed
    1. Tolias Y. A., Panas S. M. A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Transactions on Medical Imaging. 1998;17(2):263–273. doi: 10.1109/42.700738. - DOI - PubMed