Feature selection, matching, and evaluation for subcellular structure tracking.

Quan Wen, Jean Gao, Kate Luby-Phelps

Research output: Contribution to journalArticle

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.

Fingerprint

Feature extraction
Proteins
Visual Perception
Green Fluorescent Proteins
Organelles
Messenger RNA

ASJC Scopus subject areas

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

Cite this

@article{9d041fb179084dbab6c79d2ddbb8bdf2,
title = "Feature selection, matching, and evaluation for subcellular structure tracking.",
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.",
author = "Quan Wen and Jean Gao and Kate Luby-Phelps",
year = "2006",
language = "English (US)",
pages = "3013--3016",
journal = "Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference",
issn = "1557-170X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Feature selection, matching, and evaluation for subcellular structure tracking.

AU - Wen, Quan

AU - Gao, Jean

AU - Luby-Phelps, Kate

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84903876571&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84903876571&partnerID=8YFLogxK

M3 - Article

SP - 3013

EP - 3016

JO - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

JF - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

SN - 1557-170X

ER -