Kirschner, Matthias ; Gollmer, Sebastian T. ; Wesarg, Stefan ; Buzug, Thorsten M. (2011)
Optimal Initialization for 3D Correspondence Optimization: An Evaluation Study.
Information Processing in Medical Imaging.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The identification of corresponding landmarks across a set of training shapes is a prerequisite for statistical shape model (SSM) construction. We automatically establish 3D correspondence using one new and several known alternative approaches for consistent, shape-preserving, spherical parameterization. The initial correspondence determined by all employed methods is refined by optimizing a groupwise objective function. The quality of all models before and after optimization is thoroughly evaluated using several data sets of clinically relevant, anatomical objects of varying complexity. Correspondence quality is benchmarked in terms of the SSMs' specificity and generalization ability, which are measured using different surface based distance functions. We find that our new approach performs best for complex objects. Furthermore, all new and previously published methods of our own allow for (i) building SSMs that are significantly better than the well-known SPHARM method, (ii) establishing quasi-optimal correspondence for low and moderately complex objects without additional optimization, and (iii) considerably speeding up convergence, thus, providing means for practical, fast, and accurate SSM construction.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2011 |
| Autor(en): | Kirschner, Matthias ; Gollmer, Sebastian T. ; Wesarg, Stefan ; Buzug, Thorsten M. |
| Art des Eintrags: | Bibliographie |
| Titel: | Optimal Initialization for 3D Correspondence Optimization: An Evaluation Study |
| Sprache: | Englisch |
| Publikationsjahr: | 2011 |
| Verlag: | Springer, Berlin, Heidelberg, New York |
| Reihe: | Lecture Notes in Computer Science (LNCS); 6801 |
| Veranstaltungstitel: | Information Processing in Medical Imaging |
| Kurzbeschreibung (Abstract): | The identification of corresponding landmarks across a set of training shapes is a prerequisite for statistical shape model (SSM) construction. We automatically establish 3D correspondence using one new and several known alternative approaches for consistent, shape-preserving, spherical parameterization. The initial correspondence determined by all employed methods is refined by optimizing a groupwise objective function. The quality of all models before and after optimization is thoroughly evaluated using several data sets of clinically relevant, anatomical objects of varying complexity. Correspondence quality is benchmarked in terms of the SSMs' specificity and generalization ability, which are measured using different surface based distance functions. We find that our new approach performs best for complex objects. Furthermore, all new and previously published methods of our own allow for (i) building SSMs that are significantly better than the well-known SPHARM method, (ii) establishing quasi-optimal correspondence for low and moderately complex objects without additional optimization, and (iii) considerably speeding up convergence, thus, providing means for practical, fast, and accurate SSM construction. |
| Freie Schlagworte: | Forschungsgruppe Medical Computing (MECO), Statistical shape models (SSM), Evaluation, Optimization, Spherical parameterization, Point correspondence |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Nov 2018 11:16 |
| Letzte Änderung: | 12 Nov 2018 11:16 |
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