Different techniques developed in the previous decades are used for blood vessel detection. Different kinds of image processing approaches in the detection and analysis of blood vessels can be applied to diagnose many human diseases and help in various medical and health diagnoses. Image processing for blood vessels could be used in areas such as disease diagnosis, severity measurement of specific diseases, and in biometric security. This study compares two different techniques to accurately diagnose a specific disease according to some selective features. Diabetic retinopathy is used for this comparative study as it is one of the most severe eye disorders and chronic diseases to cause blindness. Classifications and accurate measurements for blood vessel abnormalities (exudates, hemorrhages, and micro-aneurysms) enabled the correct and accurate diagnosis in retina and diabetic retinopathy.To avoid blindness, it is essential to utilize fundus image processing application to facilitate the early discovery of a diseased retinal. Throughout the fundus automated image process, the retinal features are extracted. The techniques applied in this study are a morphological-based image processing technique and an edge detection technique using Kirsch's template. First, the application of these image processing techniques are described and explained in detail. Subsequently, a classification process is proposed to assess and evaluate the performance of each technique.