A Vascular Network Growth Estimation Algorithm Using Random Graphs

This paper presents a vascular representation and segmentation algorithm based on a multiresolution Hermite model (MHM). A two-dimensional Hermite function intensity model is developed which models blood vessel profiles in a quad-tree structure over a range of spatial resolutions. The use of a multiresolution representation simplifies the image modeling and allows for a robust analysis by combining information across scales. Estimation over scale also reduces the overall computational complexity. As well as using MHM for vessel labelling, the local image modeling can accurately represent vessel directions, widths, amplitudes, and branch points which readily enable the global topology to be inferred. An expectation-maximization (EM) type of optimization scheme is used to estimate local model parameters and an information theoretic test is then applied to select the most appropriate scale/feature model for each region of the image. In the final stage, Bayesian stochastic inference is employed for linking the local features to obtain a description of the global vascular structure. After a detailed description and analysis of MHM, experimental results on two standard retinal databases are given that demonstrate its comparative performance. These show MHM to perform comparably with other retinal vessel labelling methods.

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Analysis of Retinal Vasculature Using a Multiresolution Hermite Model

Wang

Bhalerao

Wilson

2007

IEEE Trans. Med. Imaging

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A Vascular Network Growth Estimation Algorithm Using Random Graphs

Cited by 1 publication

References 4 publications

Analysis of Retinal Vasculature Using a Multiresolution Hermite Model

Analysis of Retinal Vasculature Using a Multiresolution Hermite Model

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