Abstract
This paper presents a supervised retinal vessel segmentation by incorporating vessel filtering and wavelet transform features from orientation scores (OSs), and green intensity. Through an anisotropic wavelet-type transform, a 2D image is lifted to a 3D orientation score in the Lie-group domain of positions and orientations R2⋊S1. Elongated structures are disentangled into their corresponding orientation planes and enhanced via multi-orientation vessel filtering. In addition, scale-selective OSs (in the domain of positions, orientations and scales R2⋊S1×R+) are obtained by adding a scale adaptation to the wavelet transform. Features are optimally extracted by taking maximum orientation responses at multiple scales, to represent vessels of changing calibers. Finally, we train a Random Forest classifier for vessel segmentation. Extensive validations show that our method achieves a competitive segmentation, and better vessel preservation with less false detections compared with the state-of-the-art methods.
Original language | English |
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Pages (from-to) | 107-123 |
Journal | Pattern Recognition |
Volume | 69 |
DOIs | |
Publication status | Published - 2017 |
Keywords
- Orientation score (OS)
- Random forest
- Retinal image
- Vessel segmentation
- Wavelet transform