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Computer methods and image processing provide medical doctors assistance atany time and relieve their workload, especially for iterative processes likeidentifying objects of interest such as lesions and anatomical structures fromthe image. Vascular detection is considered to be a crucial step in someretinal image analysis algorithms to find other retinal landmarks and lesions,and their corresponding diameters, to use as a length reference to measureobjects in the retina. The objective of this study is to compare the effect oftwo preprocessing methods on retinal vessel segmentation methods,Laplacian-of-Gaussian edge detector (using second-order spatialdifferentiation), Canny edge detector (estimating the gradient intensity), andMatched filter edge detector either in the normal fundus images or in thepresence of retinal lesions like diabetic retinopathy. From the accuracyviewpoint, compared to manual segmentation performed by ophthalmologists forretinal images belonging to a test set of 120 images, by using firstpreprocessing method, Illumination equalization, and contrast enhancement, theaccuracy of Canny, Laplacian-of-Gaussian, and Match filter vessel segmentationwas more than 85% for all databases (MUMS-DB, DRIVE, MESSIDOR). The performanceof the segmentation methods using top-hat preprocessing (the second method) wasmore than 80%. And lastly, using matched filter had maximum accuracy for thevessel segmentation for all preprocessing steps for all databases.

Comparing Different Preprocessing Methods in Automated Segmentation of Retinal Vasculature