Abstract
The Gegenbauer image reconstruction method, previously shown to improve the quality of magnetic resonance images, is utilized in this study as a segmentation preprocessing step. It is demonstrated that, for all simulated and real magnetic resonance images used in this study, the Gegenbauer reconstruction method improves the accuracy of segmentation. Although it is more desirable to use the k-space data for the Gegenbauer reconstruction method, only information acquired from MR images is necessary for the reconstruction, making the procedure completely self-contained and viable for all human brain segmentation algorithms.
| Original language | English |
|---|---|
| Pages (from-to) | 489-502 |
| Number of pages | 14 |
| Journal | NeuroImage |
| Volume | 20 |
| Issue number | 1 |
| DOIs | |
| State | Published - Sep 1 2003 |
| Externally published | Yes |
Funding
The authors of this work were supported in part by the Arizona Center for Alzheimer’s Disease Research (R. Archibald, K. Chen, A. Gelb, and R. Renaut), the Center for System Science and Engineering Research at Arizona State University (R. Archibald and A. Gelb), the Department of Mathematics and Statistics at Arizona State University (R. Archibald, K. Chen, A. Gelb, and R. Renaut), the John von Neumann visiting Professorship of the Zentrum Mathematik, Technische Universitaet Muenchen (R. Renaut), NSF Grant DMS 9977234 (R. Renaut), and NSF Grant DMS 01-07428 (A. Gelb).
| Funders | Funder number |
|---|---|
| Arizona Center for Alzheimer’s Disease Research | |
| Center for System Science and Engineering Research at Arizona State University | |
| Zentrum Mathematik | |
| National Science Foundation | DMS 01-07428, DMS 9977234 |
| Technische Universität München |
Keywords
- Brain extraction
- Edge detection
- Gegenbauer reconstruction
- Noise
- Segmentation