Journal
JOURNAL OF STRUCTURAL BIOLOGY
Volume 175, Issue 3, Pages 319-328Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jsb.2011.05.017
Keywords
Support vector machine; Texture analysis; Particle boxing
Funding
- US Department of Energy, Office of Science, Office of Biological and Environmental Research
- Genomics: GTL Foundational Science [DE-AC02-05CH11231]
- US Department of Energy
- Lawrence Berkeley National Laboratory
Ask authors/readers for more resources
The goal of this study is to evaluate the performance of software for automated particle-boxing, and in particular the performance of a new tool (TextonSVM) that recognizes the characteristic texture of particles of interest. As part of a high-throughput protocol, we use human editing that is based solely on class-average images to create final data sets that are enriched in what the investigator considers to be true-positive particles. The Fourier shell correlation (FSC) function is then used to characterize the homogeneity of different single-particle data sets that are derived from the same micrographs by two or more alternative methods. We find that the homogeneity is generally quite similar for class-edited data sets obtained by the texture-based method and by SIGNATURE, a cross-correlation-based method. The precision recall characteristics of the texture-based method are, on the other hand, significantly better than those of the cross-correlation based method; that is to say, the texture-based approach produces a smaller fraction of false positives in the initial set of candidate particles. The computational efficiency of the two approaches is generally within a factor of two of one another. In situations when it is helpful to use a larger number of templates (exemplars), however, TextonSVM scales in a much more efficient way than do boxing programs that are based on localized cross-correlation. (C) 2011 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available