DocumentCode :
471782
Title :
Feature Selection, Matching, and Evaluation for Subcellular Structure Tracking
Author :
Wen, Quan ; Gao, Jean ; Luby-Phelps, Kate
Author_Institution :
Dept. of Comput. Sci. & Eng., Texas Univ., Arlington, TX
fYear :
2006
fDate :
Aug. 30 2006-Sept. 3 2006
Firstpage :
3013
Lastpage :
3016
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
Keywords :
biological techniques; cell motility; feature extraction; fluorescence; image matching; image segmentation; molecular biophysics; proteins; Gestalt visual perception theory; cell regulation; cellular structure segmentation; confocal video data; feature consistence rate; feature convergence rate; feature matching; feature selection; green fluorescent protein labeled subcellular structure; grid-based minimum variance feature selection method; mRNA; mean minimum to maximum ratio similarity measure; organelles; proteins; subcellular particle motility; subcellular structure tracking; vesicles; Active contours; Biological system modeling; Cells (biology); Convergence; Fluorescence; Noise shaping; Particle tracking; Protein engineering; Robustness; Visual perception;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
Conference_Location :
New York, NY
ISSN :
1557-170X
Print_ISBN :
1-4244-0032-5
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/IEMBS.2006.259936
Filename :
4462431
Link To Document :
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