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Abstract
This paper presents an improved tracking based method for retinal vessel segmentation that uses blood vessel morphology to adapt the tracking parameters. The method includes branching detection and avoidance methods. A bi-level threshold method, based on local vessel information, is used for segmentation. Tracking is based on Kalman filtering. The results are compared with existing ground truth. It is concluded that ground truth segmentation is not easily comparable.
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Authors and Affiliations
Department of Physics Strand, King’s College London, London, England
Pedro Quelhas & James Boyce
IDIAP, Dalle Molle Institute for Perceptual Artificial Intelligence, Rue Du Simplon 4, Martigny, Switzerland
Pedro Quelhas
- Pedro Quelhas
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- James Boyce
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Editors and Affiliations
Unitat de Gràfics i Visió per Ordinador Departament de Ciències Matemàtiques i Informàtica, Universitat de les Illes Balears Edifici Anselm Turmeda, Ctra. de Valldemossa km 7,5, 07122, Palma de Mallorca, Spain
Francisco José Perales
FEUP - Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal
Aurélio J. C. Campilho
Departamento de Ciencias da la Computacíon e I.A., Universidad de Granada, E.T. S. Ing. Informática, 18071, Granada, Spain
Nicolás Pérez de la Blanca
Dept. System Engineering and Automation, Universitat Politècnica de Catalunya (UPC) Barcelona, Spain
Alberto Sanfeliu
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© 2003 Springer-Verlag Berlin Heidelberg
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Quelhas, P., Boyce, J. (2003). Vessel Segmentation and Branching Detection Using an Adaptive Profile Kalman Filter in Retinal Blood Vessel Structure Analysis. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_93
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