Name
Abstract | ||
|---|---|---|
MR Imaging techniques provide a non-invasive and accurate method for determining the ultra-structural features of human anatomy. In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. Our approach is based on an automated landmark extraction algorithm which automatically selects points along the contour of the ventricles from a series of 2D MRI brain images. Automated landmark extraction is accomplished through the use of the self-organising network the growing neural gas (GNG) which is able to topographically map the low dimension of the network to the high dimension of the manifold of the contour without requiring a priori knowledge of the structure of the input space. The GNG method is compared to other self-organising networks such as Kohonen and Neural Gas (NG) maps and an error metric is applied to quantify the performance of our algorithm compared to the other two. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11569541_22 | CVBIA |
Keywords | Field | DocType |
mr imaging technique,self-organising network,medical shape,neural gas network,high resolution,t1-weighted mr image,accurate method,neural gas,high dimension,automated landmark extraction,automated landmark extraction algorithm,automatic landmarking,gng method,a priori knowledge,topographic map,brain imaging | Computer vision,Pattern recognition,Landmark point,Computer science,Extraction algorithm,Minimum description length,A priori and a posteriori,Self-organizing map,Artificial intelligence,Landmark,Manifold,Neural gas | Conference |
Volume | ISSN | ISBN |
3765 | 0302-9743 | 3-540-29411-2 |
Citations | PageRank | References |
12 | 0.83 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anastassia Angelopoulou | 1 | 102 | 21.29 |
| Alexandra Psarrou | 2 | 199 | 27.14 |
| José Garcia Rodriguez | 3 | 55 | 9.71 |
| Kenneth Revett | 4 | 313 | 27.15 |