Segmentation is a preliminary but important stage in most applications that use medical image data. The work in this thesis mainly focuses on branching structure segmentation on 2D retinal images, by applying image processing and statistical pattern recognition techniques. This thesis presents a vascular modelling algorithm based on a multiresolution image representation. A 2D Hermite polynomial is introduced to model the blood vessel profile in a quad-tree structure over a range of spatial/spatial-frequency resolutions. The use of a multi-resolution representation allows robust analysis by combining information across scales and to help improve computational efficiency. A Fourier based modelling and estimation process is developed, followed by an EM type of optimisation scheme to estimate model parameters. An information based process is then presented to select the most appropriate scale/model for modelling each region of the image. In the final stage, a deterministic graph theoretic approach and a stochastic approach within a Bayesian framework are employed for linking the local features and inferring the global vascular structure. Experimental results on a number of retinal images have been shown to demonstrate the effective application of the proposed algorithms. Some preliminary results on 3D data are also presented showing the possible extension of the algorithms.