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Experimental evaluation of support vector machine-based and correlation-based approaches to automatic particle selection.

Abstract：

: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.

journal_name

J Struct Biol

journal_title

Journal of structural biology

authors

Arbeláez P,Han BG,Typke D,Lim J,Glaeser RM,Malik J

doi

10.1016/j.jsb.2011.05.017

subject

Has Abstract

pub_date

2011-09-01 00:00:00

pages

319-28

issue

3

eissn

1047-8477

issn

1095-8657

pii

S1047-8477(11)00165-1

journal_volume

175

pub_type

杂志文章

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