University of Illinois Urbana-Champaign

Automated Cell-Tracking System Using Locally Laplacian Shift Features Detecting Cell Growth on Slanted Nano-Pillar Structure

Li, Xing

This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.

Permalink

https://hdl.handle.net/2142/47620

Description

Title
Automated Cell-Tracking System Using Locally Laplacian Shift Features Detecting Cell Growth on Slanted Nano-Pillar Structure
Author(s)
Li, Xing
Contributor(s)
  • Liu, Logan
  • Hsia, Jimmy K.
Issue Date
2013-05
Keyword(s)
  • cell-tracking
  • Laplacian scale-invariant features transform (SIFT)
  • cell-detection Laplacian SIFT
  • nano-pillar
  • DIC microscopy
Abstract
In this project, a novel cell-tracking scheme using Laplacian scale-invariant features transform (SIFT) is proposed for automatic cell motion analysis in grayscale microscopic videos, particularly for mouse muscle cells (C2C12) growing on our slanted Nano-pillar structures. In this proposed novel approach, cells were successfully detected in the microscopic images and a locality detecting algorithm using Laplacian SIFT is also proposed to track the SIFT feature points along successive frames of low-contrast differential interference contrast (DIC) microscopy videos. Experiments on low-contrast DIC microscopy videos of hundreds of healthy living-cells grow on slanted Nano-pillar structure materials show that comparing with based SIFT tracking, the proposed locally Laplacian SIFT can significantly reduce the error, improve the robustness and save the run-time. With this novel scheme, further experimental results demonstrate that living-cells have particular preferred moving directions and motion patterns related to the degree of the slanted Nano-pillar materials. Thus, the proposed algorithm is a robust and accurate approach to tackling the challenge of living-cells tracking in DIC microscopy.
Type of Resource
text
Language
en
Permalink
http://hdl.handle.net/2142/47620

Owning Collections

Senior Theses - Electrical and Computer Engineering PRIMARY
The best of ECE undergraduate research