Abstract
Objectives: A particular problem in image registration arises for multi-modal images taken from different imaging devices and/or modalities. Starting in 1995, mutual information has shown to be a very successful distance measure for multi-modal image registration. Therefore, mutual information is considered to be the state-of-the-art approach to multi-modal image registration. However, mutual information has also a number of well-known drawbacks. Its main disadvantage is that it is known to be highly non-convex and has typically many local maxima. Methods: This observation motivates us to seek a different image similarity measure which is better suited for optimization but as well capable to handle multimodal images. Results: In this work, we investigate an alternative distance measure which is based on normalized gradients. Conclusions: As we show, the alternative approach is deterministic, much simpler, easier to interpret, fast and straightforward to implement, faster to compute, and also much more suitable to numerical optimization.
Originalsprache | Englisch |
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Titel | Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006 |
Seitenumfang | 8 |
Band | 46 |
Erscheinungsort | Berlin, Heidelberg |
Herausgeber (Verlag) | Springer Verlag |
Erscheinungsdatum | 30.05.2007 |
Auflage | 3 |
Seiten | 292-299 |
ISBN (Print) | 978-3-540-44727-6 |
ISBN (elektronisch) | 978-3-540-44728-3 |
DOIs | |
Publikationsstatus | Veröffentlicht - 30.05.2007 |
Veranstaltung | International Conference on Medical Image Computing and Computer-Assisted Intervention 2006 - Copenhagen, Dänemark Dauer: 01.10.2006 → 06.10.2006 |