Abstract
The paradigm of Fuzzy Morphology extends the concept of binary morphology to handle grayscale images. Fuzzy Morphology provides meaningful, local and simple operations that, when properly combined, form powerful transformations. We use this approach to segment out vessels in eye-fundus images, which can be used to diagnose medical conditions such as diabetic retinopathy. To automatically estimate the presence of such conditions, distinguishing vessels from other artifacts becomes a necessary initial step. To address the problem of segmenting curvilinear-like objects such as vessels, our methodology consists on applying the same structuring element rotated several times. We construct a vessel segmentation method and compare it with current state-of-the-art alternatives, showing the potential of our approach.
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Notes
- 1.
We use the concept of reflection with respect to the center of an image B as its rotation \(180^{\circ }\) around the central point. According to our notation, and assuming that B has size \(n_1 \times n_2\), it is formally defined as \(\overline{B}(x_1, x_2) = B(n_1 + 1 - x_1, n_2 + 1 - x_2)\). Although the definition is general, we will only consider structuring elements with \(n_1, n_2\) odd.
- 2.
Leave-one-out consists on training a method with all samples but one, and evaluate the trained classifier to the last one. This procedure is repeated with each sample to obtain all segmentations. Leave-one-out is useful when we have very few samples because the estimation given is realistic: to evaluate one sample, its ground truth is never used.
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Aknowledgments
This work was partially supported by the projects TIN2013-42795-P and TIN2014-56381-REDT (LODISCO network). P. Bibiloni also benefited from the fellowship FPI/1645/2014 of the Conselleria d’Educació, Cultura i Universitats of the Govern de les Illes Balears under an operational program co-financed by the European Social Fund.
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Bibiloni, P., González-Hidalgo, M., Massanet, S. (2015). Retinal Vessel Detection Based on Fuzzy Morphological Line Enhancement. In: Puerta, J., et al. Advances in Artificial Intelligence. CAEPIA 2015. Lecture Notes in Computer Science(), vol 9422. Springer, Cham. https://doi.org/10.1007/978-3-319-24598-0_6
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