- Base methods:
- [1] Kernel Boost
that learns discriminative convolutional filters based on the Gradient Boosting framework.
- [2] Optimally Oriented Flux (OOF), an unsupervised
method with manually designed filter that is widely used for delineating tubular structures.
- [3] IUWT , an unsupervised
method based on isotropic undecimated wavelet transform.
- [4] Eigen,
a multiscale Hessian-based method.
- [5] T2T, a supervised method based on pixel classification,
medial sub-tree generation, and global tree linking components.
-
Performance on the retinal testbeds DRIVE & STARE with three different base methods
|
Kernel Boost |
OOF |
IUWT |
Base |
76.29 & 78.77 |
69.97 & 72.86 |
64.13 & 72.20 |
Ours-nCnM |
79.12 & 79.63 |
71.22 & 73.28 |
71.79 & 74.15 |
Ours-MnC |
79.44 & 81.14 |
71.93 & 75.91 |
72.25 & 74.29 |
Ours |
80.11 & 81.93 |
72.12 & 76.84 |
72.65 & 74.58 |
Here the modified F1 measure (%) is used. Ours-nCnM refers to the simplified variant of our approach without using completion fields
and matting, while Ours-MnC instead refers to without completion field but with matting. Ours is the full version of our approach.
-
Performance on 2D neronal testbed NeuB1 with four different base methods
|
Kernel Boost |
OOF |
Eigen |
T2T |
Base |
84.74 |
63.50 |
63.94 |
66.49 |
Ours-nMnC |
86.20 |
65.99 |
66.81 |
66.18 |
Ours-MnC |
86.39 |
70.25 |
70.38 |
75.70 |
Ours |
86.80 |
71.03 |
71.89 |
76.01 |
- References :
[1] C. Becker, R. Rigamonti, V. Lepetit, and P. Fua. Supervised feature learning for curvilinear structure segmentation. In MICCAI, 2013.
[2] M. Law and A. Chung. Three dimensional curvilinear structure detection using optimally oriented flux. In ECCV, 2008.
[3] P. Bankhead, C. Scholfield, J. McGeown, and T. Curtis. Fast retinal vessel detection and measurement using wavelets and edge location refinement. PLoS ONE, 2012.
[4] A. Frangi, W. Niessen, K. Vincken, and M. Viergever. Multiscale vessel enhancement filtering. In MICCAI, 1998.
[5] S. Basu, A. Aksel, B. Condron, and S. Acton. Tree2Tree: Neuron segmentation for generation of neuronal morphology. In ISBI, 2010.
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