Title | ||
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Refined localization of three-dimensional anatomical point landmarks using multistep differential approaches |
Abstract | ||
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In this contribution, we are concerned with the detection and refined localization of 3D point landmarks. We propose multi-step differential procedures which are generalizations of an existing two-step procedure for subpixel localization of 2D point landmarks. This two-step procedure combines landmark detection by applying a differential operator with refined localization through a differential edge intersection approach. In this paper, we theoretically analyze the localization performance of this procedure for analytical models of a Gaussian blurred L-corner as well as a Gaussian blurred ellipse. By varying the model parameters differently tapered and curved structures are represented. The results motivate the use of an analogous procedure for 3D point landmark localization. We generalize the edge intersection approach to 3D and, by combining it with 3D differential operators for landmark detection, we propose three multi-step procedures for subvoxel localization of 3D point landmarks. The multi-step procedures are experimentally tested for 3D synthetic images and 3D MR images of the human head. We show that the multi-step procedures significantly improve the localization accuracy in comparison to applying a 3D detection operator alone. |
Year | DOI | Venue |
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1998 | 10.1117/12.310892 | PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE) |
Keywords | Field | DocType |
3D anatomical point landmarks,human brain,3D differential operators,3D edge intersection approach,point landmark localization,point-based image registration | Computer vision,Edge detection,Algorithm,Differential operator,Gaussian,Operator (computer programming),Artificial intelligence,Subpixel rendering,Landmark,Ellipse,Anatomical point,Mathematics | Conference |
Volume | ISSN | Citations |
3338 | 0277-786X | 5 |
PageRank | References | Authors |
0.54 | 0 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
soenke frantz | 1 | 5 | 0.54 |
Karl Rohr | 2 | 377 | 52.96 |
H. Siegfried Stiehl | 3 | 516 | 67.10 |