In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network (“Frangi-Net“), and illustrate that the Frangi-Net is equivalent to the original Frangi filter. Furthermore, we show that, as a neural network, Frangi-Net is trainable. We evaluate the proposed method on a set of 45 high resolution fundus images. After fine-tuning, we observe both qualitative and quantitative improvements in the segmentation quality compared to the original Frangi measure, with an increase up to 17% in F1 score.
CITATION STYLE
Fu, W., Breininger, K., Schaffert, R., Ravikumar, N., Würfl, T., Fujimoto, J., … Maier, A. (2018). Frangi-net. In Informatik aktuell (Vol. 0, pp. 341–346). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-662-56537-7_87
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