title: Live-cell tracking using SIFT features in DIC microscopic videos.
abstract: 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.
authors.affiliation: Department of Computer Science, Loughborough University, Loughborough LB113TU, UK. m.jiang@lboro.ac.uk
authors.forename: Richard M
authors.initials: RM
authors.lastname: Jiang
authors.forename: Danny
authors.initials: D
authors.lastname: Crookes
authors.forename: Nie
authors.initials: N
authors.lastname: Luo
authors.forename: Michael W
authors.initials: MW
authors.lastname: Davidson
mesh_terms: D000465:Algorithms; D000818:Animals; D002460:Cell Line; D002465:Cell Movement / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008857:Microscopy, Interference / Q000379:methods*; D008858:Microscopy, Phase-Contrast; D018715:Microscopy, Video / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis