Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | F1 score,Vessel segmentation,Pattern recognition,Segmentation,Computer science,Fundus (eye),Artificial intelligence,Artificial neural network |
DocType | Volume | Citations |
Journal | abs/1711.03345 | 3 |
PageRank | References | Authors |
0.44 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weilin Fu | 1 | 4 | 3.87 |
| Katharina Breininger | 2 | 3 | 5.85 |
| Tobias Würfl | 3 | 52 | 10.53 |
| Nishant Ravikumar | 4 | 4 | 4.21 |
| Roman Schaffert | 5 | 3 | 1.80 |
| Andreas K. Maier | 6 | 560 | 178.76 |