Digital fundus imaging is becoming an important task in computer-aided diagnosis and has gained an important position in the digital medical imaging domain. One of its applications is the retinal blood vessels extracting. Object detection in machine vision and image processing has gained increasing interest due to its social and security potential. Plenoptic imaging is a promising optical technique. This technique computes the location and the propagation direction information of the object light, which are used as efficient descriptors to detect and track the object displacement. In this chapter, the authors use an edge detection technique to extract and segment blood vessels in the retinal image. They propose a novel approach to detect vessels in a simulated light fields fundus image, based on the image representation with the first and the second order derivative, well known as gradient and Laplacian image descriptors. Since the difficulties to get a light field image of a fundus in the retinal image, the authors test their model in the image provided by Sha Tong et al.
Retinal blood vessels segmentation using classical edge detection filters and the neural network
