Name
Title | ||
|---|---|---|
Automatically building 2D statistical shapes using the topology preservation model GNG |
Abstract | ||
|---|---|---|
Image segmentation is very important in computer based image interpretation and it involves the labeling of the image so that the labels correspond to real world objects. In this study, we utilise a novel approach to automatically segment out the ventricular system from a series of MR brain images and to recover the shape of hand outlines from a series of 2D training images. Automated landmark extraction is accomplished through the use of the self-organising model the growing neural gas (GNG) network which is able to learn and preserve the topological relations of a given set of input patterns without requiring a priori knowledge of the structure of the input space. The GNG based method is compared to other self-organising networks such as Kohonen and Neural Gas (NG) maps and results are given showing that the proposed method preserves accurate models. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11612032_53 | asian conference on computer vision |
Keywords | Field | DocType |
input pattern,self-organising network,self-organising model,image interpretation,input space,Automatically building,training image,neural gas,statistical shape,proposed method,topology preservation model,MR brain image,image segmentation | Topology,Computer science,Self-organization,A priori and a posteriori,Image processing,Self-organizing map,Image segmentation,Landmark,Artificial neural network,Neural gas | Conference |
Volume | ISSN | ISBN |
3851 | 0302-9743 | 3-540-31219-6 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Garcia Rodriguez | 1 | 55 | 9.71 |
| Anastassia Angelopoulou | 2 | 102 | 21.29 |
| Alexandra Psarrou | 3 | 199 | 27.14 |
| Kenneth Revett | 4 | 313 | 27.15 |