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Related Experiment Video

Updated: Jun 25, 2026

Fluorescence-mediated Tomography for the Detection and Quantification of Macrophage-related Murine Intestinal Inflammation
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Fluorescence-mediated Tomography for the Detection and Quantification of Macrophage-related Murine Intestinal Inflammation

Published on: December 15, 2017

Quantitative stain detection in vivo using fluorescent imaging.

Andrew M Taylor1, Roger P Ellwood, Iain A Pretty

  • 1The University of Manchester, England, United Kingdom.

Journal of Dentistry
|March 3, 2009
PubMed
Summary
This summary is machine-generated.

A new algorithm analyzes Quantitative Light-induced Fluorescence (QLF) images to detect tooth stain in vivo. This method overcomes limitations of previous techniques, offering objective and reproducible stain assessment for clinical trials.

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Area of Science:

  • Biophotonics
  • Dental Research
  • Image Analysis

Background:

  • Quantitative Light-induced Fluorescence (QLF) is effective for in vitro tooth stain measurement.
  • Current QLF analysis requires unstained tissue for region definition, limiting in vivo application.

Purpose of the Study:

  • Develop and test a novel algorithm for in vivo tooth stain detection using QLF imaging.
  • Overcome the limitations of existing QLF analysis methods for clinical use.

Main Methods:

  • An algorithm was created using convex hulls to reconstruct unstained tooth images from QLF data.
  • The algorithm subtracts the reconstructed image from the captured image to identify stained areas.
  • A tooth outline (mask) is required to guide the image reconstruction process.

Main Results:

  • The developed algorithm successfully detected tooth stain on in vivo QLF images.
  • Algorithm performance was evaluated using various threshold parameters on images from a Chlorhexidine stain study.
  • Software output showed strong correlation with manual scoring (Pearson's r > 0.7, p < 0.001).

Conclusions:

  • The novel algorithm enables objective and reproducible in vivo tooth stain assessment using QLF.
  • This method offers significant advantages over subjective clinical visual assessments.
  • The algorithm shows promise for use in large-scale patient studies and clinical trials.