Vessel network detection using contour evolution and color components [electronic resource].
- Berkeley, Calif. : Lawrence Berkeley National Laboratory, 2011.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
- Physical Description:
- 6 : digital, PDF file
- Additional Creators:
- Lawrence Berkeley National Laboratory
United States. Department of Energy. Office of Scientific and Technical Information
- Automated retinal screening relies on vasculature segmentation before the identification of other anatomical structures of the retina. Vasculature extraction can also be input to image quality ranking, neovascularization detection and image registration, among other applications. There is an extensive literature related to this problem, often excluding the inherent heterogeneity of ophthalmic clinical images. The contribution of this paper relies on an algorithm using front propagation to segment the vessel network. The algorithm includes a penalty in the wait queue on the fast marching heap to minimize leakage of the evolving interface. The method requires no manual labeling, a minimum number of parameters and it is capable of segmenting color ocular fundus images in real scenarios, where multi-ethnicity and brightness variations are parts of the problem.
- Published through SciTech Connect.
32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
Ushizima, Daniela; Medeiros, Fatima; Martins, Charles; Cuadros, Jorge.
Computational Research Division
- Funding Information:
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