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Abstract
The detection of vascular bifurcations in retinal fundus images is important for finding signs of various cardiovascular diseases. We propose a novel method to detect such bifurcations. Our method is implemented in trainable filters that mimic the properties of shape-selective neurons in area V4 of visual cortex. Such a filter is configured by combining given channels of a bank of Gabor filters in an AND-gate-like operation. Their selection is determined by the automatic analysis of a bifurcation feature that is specified by the user from a training image. Consequently, the filter responds to the same and similar bifurcations. With only 25 filters we achieved a correct detection rate of 98.52% at a precision rate of 95.19% on a set of 40 binary fundus images, containing more than 5000 bifurcations. In principle, all vascular bifurcations can be detected if a sufficient number of filters are configured and used.
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Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, The Netherlands
George Azzopardi & Nicolai Petkov
- George Azzopardi
Search author on:PubMed Google Scholar
- Nicolai Petkov
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Dpto. Matematica Aplicada I, Escuaela Técnica Superior de Ingeniería Informática, Universite de Sevilla, Avda. Reina Mercedes, s/n, 41012, Sevilla, Spain
Pedro Real
Departamento de Matemática Aplicada I, Escuela Técnica Superior de Ingeniería Informática, University of Seville, Avenida Reina Mercedes s/n, 41012, Sevilla, Spain
Daniel Diaz-Pernil & Helena Molina-Abril &
Departamento de Didáctica de la Mathemática y de las CC.Experimentales, Universidad del País Vasco-Esukal Herriko Unibertsitatea, Escuela Universitaria de Magisterio, Ramón y Cajal, 72, 48014, Bilbao (Bizcaia), Spain
Ainhoa Berciano
Institute of Computer Graphics and Algorithms, Pattern Recognition and Image Processing Group, Vienna University of Technology, Favoritenstraße 9/186-3, 1040, Vienna, Austria
Walter Kropatsch
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Azzopardi, G., Petkov, N. (2011). Detection of Retinal Vascular Bifurcations by Trainable V4-Like Filters. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23672-3_55
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