Detecting branching structures using local Gaussian models
- 25 June 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
We present a method for modelling and estimating branching structures, such as blood vessel bifurcations, from medical images. Branches are modelled as a superposition of Gaussian functions in a local region which describe the amplitude, position and orientations of intersecting linear features. The centroids of component features are separated by applying K-means to the local Fourier phase and the covariances and amplitudes subsequently estimated by a likelihood maximisation. We employ a penalised likelihood test (AIC) to select the best fit model in a region. Results are presented on synthetic and representative 2D retinal images which show the estimation to be robust and accurate in the presence of noise. We compare our results with a curvature scale-space operator method.Keywords
This publication has 4 references indexed in Scilit:
- Inferring Vascular Structure from 2D and 3D ImageryPublished by Springer Nature ,2001
- Semi-automated tabulation of the 3D topology and morphology of branching networks using CT: application to the airway treePhysics in Medicine & Biology, 1999
- Junction detection with automatic selection of detection scales and localization scalesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1994
- Corner detectionPattern Recognition, 1990