Feature selection, matching, and evaluation for subcellular structure tracking

Quan Wen, Jean Gao, Kate Luby-Phelps

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

2 Scopus citations

Abstract

Understanding the motility of subcellular particles like organelles, vesicles, or mRNAs Is critical to understand how cells regulate delivery of specific proteins from the site of synthesis to the site of action. The goal of this paper is to present a framework of feature selection, matching, and evaluation for the segmentation and tracking of green fluorescent protein (GFP) labeled subcellular structures. To select stable and distinctive features for small-sized subcellular particles, a grid-based minimum variance (GMV) feature selection method Is proposed. To robustly keep tracking of the selected features, we propose a mean minimum to maximum ratio (MMMR) similarity measure for feature matching. In order to quantitatively evaluate the proposed methods, we define two evaluation criteria, feature convergence rate (FCVR) and feature consistence rate (FCSR), which conform with the proximity and similarity properties of Gestalt visual perception theory. Our technique was validated on real confocal video data with comparison to traditional feature selection and matching methods.

Original languageEnglish (US)
Title of host publication28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Pages3013-3016
Number of pages4
DOIs
StatePublished - Dec 1 2006
Event28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States
Duration: Aug 30 2006Sep 3 2006

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ISSN (Print)0589-1019

Other

Other28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
CountryUnited States
CityNew York, NY
Period8/30/069/3/06

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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  • Cite this

    Wen, Q., Gao, J., & Luby-Phelps, K. (2006). Feature selection, matching, and evaluation for subcellular structure tracking. In 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 (pp. 3013-3016). [4029743] (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings). https://doi.org/10.1109/IEMBS.2006.259936