Abstract
This paper presents a method for detecting and measuring the vascular structures of retinal images. Features are modelled as a superposition of Gaussian functions in a local region. The parameters i.e. centroid, orientation, width of the feature are derived by a minimum mean square error (MMSE) type of spatial regression. We employ a penalised likelihood test, the Akakie Information Criteria (AIC), to select the best model and scale for vessel segments. A maximum-cost spanning tree (MST) algorithm is then used to perform the neighbourhood linking and infer the global vascular structure. We present results of evaluations on a set of twenty digital fundus retinal images.
This work is funded by UK EPSRC (GR/M82899/01)
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Keywords
- Retinal Image
- Minimum Mean Square Error
- Ground Truth Image
- Spatial Frequency Domain
- Minimum Mean Square Error Estimation
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Wang, L., Bhalerao, A. (2003). Model Based Segmentation for Retinal Fundus Images. In: Bigun, J., Gustavsson, T. (eds) Image Analysis. SCIA 2003. Lecture Notes in Computer Science, vol 2749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45103-X_57
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DOI: https://doi.org/10.1007/3-540-45103-X_57
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