Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Detection and Quantification of Line and Sheet Structures in 3-D Images
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
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
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Automatic mammary duct detection in 3d ultrasound
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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The prevalent model for second order variation in 3-D volumes is an ellipsoid spanned by the magnitudes of the Hessian eigenvalues. Here, we describe this variation as a vector in an orthogonal shape space spanned by spherical harmonic basis functions. From this newsh ape-space, a truly rotation- and shape-invariant signal energy is defined, consistent orientation information is extracted and shape sensitive quantities are employed. The advantage of these quantities is demonstrated in detection of stenosis in Magnetic Resonance Angiography (MRA) volume. The news hape space is expected to improve both the theoretical understanding and the implementation of Hessian based analysis in other applications as well.