Information-theoretic approaches (e.g. mutual information) have yielded accurate and robust similarity measures for multi-modal and mono-modal image registration. However, recent research suggests that registration based on mutual information has room for improvement. The paper proposes a method for including spatial information in this approach by using spatial feature vectors obtained from the images. A minimum spanning tree algorithm is employed to compute an estimate of the conditional entropy in higher dimensions. The paper includes the theory to motivate the proposed similarity measure. Experimental results indicate that the suggested method can achieve a more robust and accurate registration compared to similarity measures that don't include spatial information.
|Original language||English (US)|
|Number of pages||10|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|State||Published - Dec 1 2003|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)