Live-Cell Tracking Using SIFT Features in DIC Microscopic Videos

In this paper, a novel motion-tracking scheme using scale-invariant features is proposed for automatic cell motility analysis in gray-scale microscopic videos, particularly for the live-cell tracking in low-contrast differential interference contrast (DIC) microscopy. In the proposed approach, scale...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2010-09, Vol.57 (9), p.2219-2228
Main Authors: Jiang, Richard M., Crookes, Danny, Luo, Nie, Davidson, Michael W.
Format: Article
Language:English
Subjects:
Algorithms
Animals
Applied sciences
Biological and medical sciences
Cell Line
Cell Movement - physiology
Error analysis
Exact sciences and technology
Gray-scale
Humans
Image motion analysis
Image processing
Image Processing, Computer-Assisted - methods
Information, signal and communications theory
Interference
Investigative techniques, diagnostic techniques (general aspects)
Laplace equations
Laplacian eigenmap
live-cell motion tracking
Medical sciences
microscopic cell imaging
Microscopy
Microscopy, Interference - methods
Microscopy, Phase-Contrast
Microscopy, Video - methods
Miscellaneous. Technology
Motion analysis
Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques
Pattern Recognition, Automated - methods
Principal Component Analysis
principal component analysis (PCA)
scale-invariant feature transform (SIFT)
Signal processing
Telecommunications and information theory
Tracking
Videos
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, a novel motion-tracking scheme using scale-invariant features is proposed for automatic cell motility analysis in gray-scale microscopic videos, particularly for the live-cell tracking in low-contrast differential interference contrast (DIC) microscopy. In the proposed approach, scale-invariant feature transform (SIFT) points around live cells in the microscopic image are detected, and a structure locality preservation (SLP) scheme using Laplacian Eigenmap is proposed to track the SIFT feature points along successive frames of low-contrast DIC videos. Experiments on low-contrast DIC microscopic videos of various live-cell lines shows that in comparison with principal component analysis (PCA) based SIFT tracking, the proposed Laplacian-SIFT can significantly reduce the error rate of SIFT feature tracking. With this enhancement, further experimental results demonstrate that the proposed scheme is a robust and accurate approach to tackling the challenge of live-cell tracking in DIC microscopy.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2010.2045376