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2018 | OriginalPaper | Buchkapitel

An Isotropic Minimal Path Based Framework for Segmentation and Quantification of Vascular Networks

verfasst von : Emmanuel Cohen, Laurent D. Cohen, Thomas Deffieux, Mickael Tanter

Erschienen in: Energy Minimization Methods in Computer Vision and Pattern Recognition

Verlag: Springer International Publishing

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Abstract

Minimal path approaches for image analysis aim to extract curves minimizing an energy functional. The energy of a path corresponds to its weighted curve length according to a relevant metric function. In this study, we design a binary isotropic metric model with the use of a Hessian-based vascular enhancement filter in order to extract geometrical features from vascular networks. We introduce a constrained keypoint search method able to extract subpixel vessel centrelines, diameters and bifurcations. Experiments on retinal images demonstrated that the proposed framework achieves similar even better segmentation performances as compared with methods using more sophisticated metric designs.

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Vorheriges Kapitel PointFlow: A Model for Automatically Tracing Object Boundaries and Inferring Illusory Contours

Nächstes Kapitel Limited-Memory Belief Propagation via Nested Optimization

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Titel: An Isotropic Minimal Path Based Framework for Segmentation and Quantification of Vascular Networks
verfasst von: Emmanuel Cohen
Laurent D. Cohen
Thomas Deffieux
Mickael Tanter
Verlag: Springer International Publishing
Buch: Energy Minimization Methods in Computer Vision and Pattern Recognition
Print ISBN: 978-3-319-78198-3

Electronic ISBN: 978-3-319-78199-0

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-78199-0_33

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