Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image selective smoothing and edge detection by nonlinear diffusion
SIAM Journal on Numerical Analysis
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
Geometry-Driven Diffusion in Computer Vision
Geometry-Driven Diffusion in Computer Vision
Coherence-Enhancing Diffusion Filtering
International Journal of Computer Vision
Theoretical Foundations of Anisotropic Diffusion in Image Processing
Proceedings of the 7th TFCV on Theoretical Foundations 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
CVRMed-MRCAS '97 Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery
Vesselness enhancement diffusion
Pattern Recognition Letters
Crossing-Preserving Coherence-Enhancing Diffusion on Invertible Orientation Scores
International Journal of Computer Vision
Bayesian tracking of elongated structures in 3D images
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Anisotropic plate diffusion filtering for detection of cell membranes in 3D microscopy images
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
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Filtering of vessel structures in medical images by analyzing the second order information or the Hessian of the image, is a well known technique. In this work we incorporate Frangi’s multiscale vessel filter [4], which is based on a geometrical analysis of the Hessian’ eigenvectors, into a nonlinear, anisotropic diffusion scheme, such that diffusion mainly takes place along the vessel axis while diffusion perpendicular to this axis is inhibited. The multiscale character of the vesselness filter ensures an equally good response for varying vessel radii. The first, theoretical contribution of this paper is the modification of the original formulation of this vessel filter, such that it becomes a smooth function on its domain which is a necessary condition imposed by the diffusion process to ensure well-posedness. The second contribution concerns the application of noise filtering of 3D synthetic, phantom computed tomography (CT) and patient CT data. It is shown that the method is very effective in noise filtering, illustrating its potential as a preprocessing step in the analysis of low dose CT angiography.