Retinal Vessel Segmentaion in Fundus Images
This project is a simple implementation of morphological operation based retinal vessel segmentation.
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
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.
 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.
 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).
 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.