Title: Automatic quantitative analysis and localisation of protein expression with GDF

Authors: Shuliang Wang; Ying Li; Wenchen Tu; Peng Wang

Addresses: School of Software, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian DistrictBeijing 100081, China; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China ' State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China ' International School of Software, Wuhan University, Wuhan 430079, China ' International School of Software, Wuhan University, Wuhan 430079, China

Abstract: When detecting the difference of protein expression between normal and cancerous tissues, the shape measurement of protein mostly depends on semi-automatic analysis of image software, which makes the results vulnerable to subjective factors. In this paper, GDF (generalised data field) is proposed to discover protein expression region and further locate it by taking cell nucleus as a reference. Based on the potential distribution, pixels of the image are firstly divided into different clusters. Each cluster represents protein expression in a different degree to precisely describe the details. Then, the clusters are merged into two groups under the requirements of experts or users. Meanwhile, the shapes of cell nuclei are measured, which favours the localisation of the protein expression. Compared with KM and EM, experimental results demonstrate that by using GDF, the protein can be extracted from an image easily and objectively, and the noises of background are further eliminated.

Keywords: generalised data field; GDF; clustering; difference detection; shape measurement; automatic quantitative analysis; localisation; protein expression; bioinformatics; normal tissues; cancerous tissues; cell nuclei; image analysis.

DOI: 10.1504/IJDMB.2014.064539

International Journal of Data Mining and Bioinformatics, 2014 Vol.10 No.3, pp.300 - 314

Received: 08 Jan 2013
Accepted: 10 Jan 2013

Published online: 21 Oct 2014 *

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