Abstract
Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm using wavelet-based attribute vectors defined on every image voxel. The attribute vector reflects the anatomical characteristics in a large neighborhood around the respective voxel. It plays the role of a morphological signature for each voxel and is therefore made as distinctive as possible. Correspondence is then determined via similarity of attribute vectors. Experiments with brain MR images show that the algorithm performs at least as well as human experts, even for complex cortical structures.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science |
| Editors | R.E. Ellis, T.M. Peters |
| Pages | 762-770 |
| Number of pages | 9 |
| Volume | 2879 |
| Edition | PART 2 |
| Publication status | Published - 2003 |
| Externally published | Yes |
| Event | Medical Image Computing and Computer-Assisted Intervention, MICCAI 2003 - 6th International Conference Proceedings - Montreal, Que., Canada Duration: 2003 Nov 15 → 2003 Nov 18 |
Other
| Other | Medical Image Computing and Computer-Assisted Intervention, MICCAI 2003 - 6th International Conference Proceedings |
|---|---|
| Country | Canada |
| City | Montreal, Que. |
| Period | 03/11/15 → 03/11/18 |
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ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Engineering(all)
Cite this
Correspondence detection using wavelet-based attribute vectors. / Xue, Zhong; Shen, Dinggang; Davatzikos, Christos.
Lecture Notes in Computer Science. ed. / R.E. Ellis; T.M. Peters. Vol. 2879 PART 2. ed. 2003. p. 762-770.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Correspondence detection using wavelet-based attribute vectors
AU - Xue, Zhong
AU - Shen, Dinggang
AU - Davatzikos, Christos
PY - 2003
Y1 - 2003
N2 - Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm using wavelet-based attribute vectors defined on every image voxel. The attribute vector reflects the anatomical characteristics in a large neighborhood around the respective voxel. It plays the role of a morphological signature for each voxel and is therefore made as distinctive as possible. Correspondence is then determined via similarity of attribute vectors. Experiments with brain MR images show that the algorithm performs at least as well as human experts, even for complex cortical structures.
AB - Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm using wavelet-based attribute vectors defined on every image voxel. The attribute vector reflects the anatomical characteristics in a large neighborhood around the respective voxel. It plays the role of a morphological signature for each voxel and is therefore made as distinctive as possible. Correspondence is then determined via similarity of attribute vectors. Experiments with brain MR images show that the algorithm performs at least as well as human experts, even for complex cortical structures.
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M3 - Conference contribution
AN - SCOPUS:11244283200
VL - 2879
SP - 762
EP - 770
BT - Lecture Notes in Computer Science
A2 - Ellis, R.E.
A2 - Peters, T.M.
ER -