Abstract
This paper evaluates whether shape features can be used for clustering objects in Sidec (tm), Electron Tomography (SET) reconstructions. SET reconstructions contain a large number of objects, and only a few of them are of interest. It is desired to limit the analysis to contain as few uninteresting objects as possible. Unsupervised hierarchical clustering is used to group objects into classes. Experiments are done on one synthetic data set and two data sets from a SET reconstruction of a human growth hormone (1hwg) in solution. The experiments indicate that clustering of objects in SET reconstructions based on shape features is useful for finding structural classes
Keywords
clustering; shape; electron tomography; protein
Published in
Lecture Notes in Computer Science
2005, volume: 3687, pages: 377-380
Conference
Third International Conference on Advances in Pattern Recognition, ICAPR 2005
UKÄ Subject classification
Computer graphics and computer vision
Publication identifier
- ISBN: 3-540-28833-3
Permanent link to this page (URI)
https://res.slu.se/id/publ/8353