Reconstruction of the vascular tree in retinal (ocular fundus) images is important, because it yields information such as the shape and size of individual vessels, their branching pattern and arterio-venous crossings, thereby providing information on the condition of the retina. The vascular tree is also helpful in the registration of retinal images. In this paper we describe an automated technique for detecting and reconstructing vascular trees, based on a robust detection of vessel candidates (ribbonlike features), their labelling using a neural network (NN), and a final reconstruction of the vessel tree using these labels. The NN uses vessel models automatically built during a training phase and does not rely on any explicit user specified models or sets of features.
|Title of host publication||Proc. SPIE 2167, Medical Imaging 1994: Image Processing|
|Number of pages||11|
|Publication status||Published - 1994|