  Computer methods and image processing provide medical doctors assistance at
any time and relieve their workload, especially for iterative processes like
identifying objects of interest such as lesions and anatomical structures from
the image. Vascular detection is considered to be a crucial step in some
retinal image analysis algorithms to find other retinal landmarks and lesions,
and their corresponding diameters, to use as a length reference to measure
objects in the retina. The objective of this study is to compare the effect of
two preprocessing methods on retinal vessel segmentation methods,
Laplacian-of-Gaussian edge detector (using second-order spatial
differentiation), Canny edge detector (estimating the gradient intensity), and
Matched filter edge detector either in the normal fundus images or in the
presence of retinal lesions like diabetic retinopathy. From the accuracy
viewpoint, compared to manual segmentation performed by ophthalmologists for
retinal images belonging to a test set of 120 images, by using first
preprocessing method, Illumination equalization, and contrast enhancement, the
accuracy of Canny, Laplacian-of-Gaussian, and Match filter vessel segmentation
was more than 85% for all databases (MUMS-DB, DRIVE, MESSIDOR). The performance
of the segmentation methods using top-hat preprocessing (the second method) was
more than 80%. And lastly, using matched filter had maximum accuracy for the
vessel segmentation for all preprocessing steps for all databases.
