Intrinsic Classification of Single Particle Images by Spectral Clustering

Yutaka Ueno, Masaki Kawata, Shinji Umeyama

Abstract

An application of spectral clustering to single particle analysis of a biological molecule is described. Using similarity scores for the given data set, clustering was performed in a factor space made by the eigenvector of the normalized similarity matrix. Image data was thus classified by means of information intrinsic to the ensemble of given data. The method was tested on a simulated transmission electron microscopy image and a real image data set of 70S ribosome. The average images of clusters were obtained by iterative alignment, which successfully represented characteristic views of the target molecules. Comparisons with traditional methods and techniques in practical implementation are discussed.

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Paper Citation


in Harvard Style

Ueno Y., Kawata M. and Umeyama S. (2005). Intrinsic Classification of Single Particle Images by Spectral Clustering . In Proceedings of the 1st International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2005) ISBN 972-8865-35-X, pages 60-67. DOI: 10.5220/0001193800600067


in Bibtex Style

@conference{bpc05,
author={Yutaka Ueno and Masaki Kawata and Shinji Umeyama},
title={Intrinsic Classification of Single Particle Images by Spectral Clustering},
booktitle={Proceedings of the 1st International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2005)},
year={2005},
pages={60-67},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001193800600067},
isbn={972-8865-35-X},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2005)
TI - Intrinsic Classification of Single Particle Images by Spectral Clustering
SN - 972-8865-35-X
AU - Ueno Y.
AU - Kawata M.
AU - Umeyama S.
PY - 2005
SP - 60
EP - 67
DO - 10.5220/0001193800600067