The paper presents a method for automated segmentation of retinal vasculature using a 2-D Morlet wavelet and supervised classification. The method classifies each pixel as either a *vessel* or *non-vessel* based on its feature vector, which includes the pixel's intensity and continuous 2-D Morlet wavelet transform responses at multiple scales. The Morlet wavelet is effective for noise filtering and vessel enhancement due to its ability to tune to specific frequencies. A Bayesian classifier with Gaussian mixture models is used for fast and accurate classification, while a linear minimum squared error classifier is also tested for comparison. The method is evaluated on the DRIVE and STARE databases of manually labeled non-mydriatic images, achieving an area under the ROC curve of 0.9598 for the DRIVE database and 0.9651 for the STARE database, outperforming previous methods. The paper discusses the advantages and limitations of the approach, including its computational efficiency and performance in handling different types of images and lighting conditions. Future work aims to address challenges such as false detection of noise and artifacts, and the inability to capture very thin vessels.The paper presents a method for automated segmentation of retinal vasculature using a 2-D Morlet wavelet and supervised classification. The method classifies each pixel as either a *vessel* or *non-vessel* based on its feature vector, which includes the pixel's intensity and continuous 2-D Morlet wavelet transform responses at multiple scales. The Morlet wavelet is effective for noise filtering and vessel enhancement due to its ability to tune to specific frequencies. A Bayesian classifier with Gaussian mixture models is used for fast and accurate classification, while a linear minimum squared error classifier is also tested for comparison. The method is evaluated on the DRIVE and STARE databases of manually labeled non-mydriatic images, achieving an area under the ROC curve of 0.9598 for the DRIVE database and 0.9651 for the STARE database, outperforming previous methods. The paper discusses the advantages and limitations of the approach, including its computational efficiency and performance in handling different types of images and lighting conditions. Future work aims to address challenges such as false detection of noise and artifacts, and the inability to capture very thin vessels.