Automatic segmentation and band detection of protein images based on the standard deviation profile and its derivative

Yassin Labyed, Naima Kaabouch, Richard R. Schultz, Brij B. Singh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Gel electrophoresis has significantly influenced the progress achieved in genetic studies over the last decade. Image processing techniques that are commonly used to analyze gel electrophoresis images require mainly three steps: band detection, band matching, and quantification and comparison. Although several techniques have been proposed to fully automate all steps, errors in band detection and, hence, in quantification are still important issues to address. In order to detect bands, many techniques were used, including image segmentation. In this paper, we present two novel, fully-automated techniques based on the standard deviation and its derivative to perform segmentation and to detect protein bands. Results show that even for poor quality images with faint bands, segmentation and detection are highly accurate.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Electro/Information Technology, EIT 2007
Pages577-582
Number of pages6
DOIs
StatePublished - Dec 1 2007
Externally publishedYes
Event2007 IEEE International Conference on Electro/Information Technology, EIT 2007 - Chicago, IL, United States
Duration: May 17 2007May 20 2007

Publication series

Name2007 IEEE International Conference on Electro/Information Technology, EIT 2007

Other

Other2007 IEEE International Conference on Electro/Information Technology, EIT 2007
CountryUnited States
CityChicago, IL
Period5/17/075/20/07

Keywords

  • Band detection
  • Gel electrophoresis image
  • Protein
  • Segmentation

ASJC Scopus subject areas

  • Computer Science(all)
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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