Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Three Dimensional Curvilinear Structure Detection Using Optimally Oriented Flux
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Efficient implementation for spherical flux computation and its application to vascular segmentation
IEEE Transactions on Image Processing
A Deformable Surface Model for Vascular Segmentation
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
An oriented flux symmetry based active contour model for three dimensional vessel segmentation
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Global Minimization for Continuous Multiphase Partitioning Problems Using a Dual Approach
International Journal of Computer Vision
Tubular Structure Segmentation Based on Minimal Path Method and Anisotropic Enhancement
International Journal of Computer Vision
Spinal crawlers: deformable organisms for spinal cord segmentation and analysis
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Segmentation and Quantification of Human Vessels Using a 3-D Cylindrical Intensity Model
IEEE Transactions on Image Processing
Dilated divergence based scale-space representation for curve analysis
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Hi-index | 0.00 |
Centerline extraction and segmentation of the spinal cord --- an intensity varying and elliptical curvilinear structure under strong neighboring disturbance are extremely challenging. This study proposes the gradient competition anisotropy technique to perform spinal cord centerline extraction and segmentation. The contribution of the proposed method is threefold --- 1) The gradient competition descriptor compares the image gradient obtained at different detection scales to suppress neighboring disturbance. It reliably recognizes the curvilinearity and orientations of elliptical curvilinear objects. 2) The orientation coherence anisotropy analyzes the detection responses offered by the gradient competition descriptor. It enforces structure orientation consistency to sustain strong disturbance introduced by high contrast neighboring objects to perform centerline extraction. 3) The intensity coherence segmentation quantifies the intensity difference between the centerline and the voxels in the vicinity of the centerline. It effectively removes the object intensity variation along the structure to accurately delineate the target structure. They constitute the gradient competition anisotropy method which can robustly and accurately detect the centerline and boundary of the spinal cord. It is validated and compared using 25 clinical datasets. It is demonstrated that the proposed method well suits the applications of spinal cord centerline extraction and segmentation.