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Segmentation of Retinal Vessels in Fundus Images
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Retinal Vessel Segmentaion in Fundus Images

This project is a simple implementation of morphological operation based retinal vessel segmentation.

Sample Output

Sample Result: Red - true positive, Green - false positive, Blue - false negative

Step 1: Preprocessing

This step contains green channel extraction (because retinal image has highest contrast in green channel) and gaussian filtering (for denoising).

Step 2: Toggle mapping filtering

This step is for vessel enhancement which is based on the comparison between the input image and its opening and closing


Step 3: Top-hat transformation

  • top-hat: the difference between the original image and its opening

The top-hat transform is applied so that background areas are set to zero and vessels are enhanced. The sum of several top-hat transforms is later AND with the eroded mask to get rid of the bright boundary.

In this code, the top-hat is implemented by min-max filter, i.e. erosion is min filter, dilation is max filter


Step 4: Classification

A linear normalization is then performed to reduce the effect of variance of image intensities by using the formula described in Wikipedia as:


After trying several threshold values (50, 60, 70, etc), 60 is set to get comparatively optimal segmentation performance.


This project is developed in VisionX using C and shell script.

Reference Papers

[1] Rossant, Florence, et al. "A morphological approach for vessel segmentation in eye fundus images, with quantitative evaluation." Journal of Medical Imaging and Health Informatics 1.1 (2011): 42-49.

[2] Walter T, Klein JC. Segmentation of color fundus images of the human retina: Detection of the optic disc and the vascular tree using morphological techniques”. International Symposium on Medical Data Analysis 2001 Oct 8 (pp. 282-287).

[3] Zana F, Klein JC. “Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation”. IEEE Trans. Image Processing. 2001 Jul;10(7):1010-9.

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