Name
Abstract | ||
|---|---|---|
To make up for the lack of concern on the spatial information in the conventional mutual information based image registration framework, this paper designs a novel spatial feature field, namely the maximum distance-gradient (MDG) vector field, for registration tasks. It encodes both the local edge information and globally defined spatial information related to the intensity difference, the distance, and the direction of a voxel to a MDG source point. A novel similarity measure is proposed as the combination of the multi-dimensional mutual information and an angle measure on the MDG vector field. This measure integrates both the magnitude and orientation information of the MDG vector field into the image registration process. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.media.2008.01.004 | Medical Image Analysis |
Keywords | Field | DocType |
Image registration,Multi-modality,Medical imaging,Maximum distance-gradient,Mutual information | Voxel,Spatial analysis,Computer vision,Pattern recognition,Similarity measure,Vector field,Medical imaging,Mutual information,Artificial intelligence,Image resolution,Mathematics,Image registration | Journal |
Volume | Issue | ISSN |
12 | 4 | 1361-8415 |
Citations | PageRank | References |
14 | 1.12 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Gan | 1 | 183 | 13.62 |
| Albert C. S. Chung | 2 | 964 | 72.07 |
| Shu Liao | 3 | 128 | 7.88 |