Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
A Review of Nonlinear Diffusion Filtering
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Three Dimensional Curvilinear Structure Detection Using Optimally Oriented Flux
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Model-based segmentation and motion analysis of the Thoracic Aorta from 4D ECG-gated CTA images
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Gradient competition anisotropy for centerline extraction and segmentation of spinal cords
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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This study proposes the novel dilated divergence scale-space representation for multidimensional curve-like image structure analysis. In the proposed framework, image structures are modeled as curves with arbitrary thickness. The dilated divergence analyzes the structure boundaries along the curve normal space in a multi-scale fashion. The dilated divergence based detection is formulated so as to 1) sustain the disturbance introduced by neighboring objects, 2) recognize the curve normal and tangent spaces. The latter enables the innovative formulation of structure eccentricity analysis and curve tangent space-based structure motion analysis, which have been scarcely investigated in literature. The proposed method is validated using 2D, 3D and 4D images. The structure principal direction estimation accuracies, structure scale detection accuracies and detection stabilities are quantified and compared against two scale-space approaches, showing a competitive performance of the proposed approach, under the disturbance introduced by image noise and neighboring objects. Moreover, as an application example employing the dilated divergence detection responses, an automated approach is tailored for spinal cord centerline extraction. The proposed method is shown to be versatile to well suit a wide range of applications.