Abstract
This paper evaluates whether shape features can be used for clustering objects in SidecTM 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.
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Gedda, M. (2005). Clustering of Objects in 3D Electron Tomography Reconstructions of Protein Solutions Based on Shape Measurements. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_43
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DOI: https://doi.org/10.1007/11552499_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
Online ISBN: 978-3-540-31999-3
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