Robust modelling of local image structures and its application to medical imagery

A robust modelling method for detecting and measuring isotropic, linear features and bifurcations is described and applied to analysing 2D electrophoresis and retinal images. Features are modelled as a superposition of Gaussian functions with the Hermite expansion and estimated by a combination of a multiresolution, windowed Fourier approach followed by an EM type of spatial regression. A penalised likelihood test, the Akakie information criteria (AIC) is used to select the best model and scale for feature segments. Results are shown by using samples on both gel and retinal images.