A Novel Method for Cell Phenotype Image Classification

DOI

10.2991/eeic-13.2013.24How to use a DOI?

Keywords

local difference features, protein subcellular location, classification, support vector machine

Abstract

As the development of human genomic project, the life science research has entered the post-genome era. The study of the function of the encoded proteins is one of the hotspots in life-science research and protein subcellular localization is an important basis for functional study of the protein. The most common method used for determining subcellular localization of protein in cell is fluorescence microscopy. Image feature calculation has proven invaluable in the automated cell phenotype image classification. This article proposes a novel method for cell phenotype image classification which is to count the local difference features of the fluorescence images. The novel method is tested on two image sets called LOCATE Endogenous and LOCATE Transfected. A support vector machine was trained and tested for each image set and better classification accuracies were obtained on the two image sets.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chao Li
AU  - Ji-feng Huang
PY  - 2013/12
DA  - 2013/12
TI  - A Novel Method for Cell Phenotype Image Classification
BT  - Proceedings of the 3rd International Conference on Electric and Electronics
PB  - Atlantis Press
SP  - 105
EP  - 107
SN  - 1951-6851
UR  - https://doi.org/10.2991/eeic-13.2013.24
DO  - 10.2991/eeic-13.2013.24
ID  - Li2013/12
ER  -