Retinal oxygen tension estimation in phosphorescence lifetime images using regularized least squares

Isa Yildirim*, Rashid Ansari, Justin Wanek, I. Samil Yetik, Mahnaz Shahidi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

The role of inadequate retinal oxygenation in the development of retinal diseases is an important problem for investigation. In our study, quantitative measurement of oxygen tension in retinal vasculatures is made with the noninvasive technique of phosphorescence lifetime imaging. A frequency domain approach is used to estimate the fluorescence or phosphorescence lifetime by regression analysis. Measurements are influenced by the presence of noise due to which least squares estimation yields results with high variance. Since accurate estimates are important in the study, we propose a spatial regularization method by adding a quadratic penalty term in the cost function instead of directly using classical least squares. The effectiveness of the proposed method is demonstrated by applying it to experimental and simulated data to show improved performance when compared with unregularized estimation.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Electro/Information Technology, IEEE EIT 2008 Conference
Pages465-469
Number of pages5
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2008 IEEE International Conference on Electro/Information Technology, IEEE EIT 2008 Conference - Ames, IA, United States
Duration: 18 May 200820 May 2008

Publication series

Name2008 IEEE International Conference on Electro/Information Technology, IEEE EIT 2008 Conference

Conference

Conference2008 IEEE International Conference on Electro/Information Technology, IEEE EIT 2008 Conference
Country/TerritoryUnited States
CityAmes, IA
Period18/05/0820/05/08

Keywords

  • Least squares
  • Phosphorescence lifetime imaging
  • Regularization
  • Retinal image analysis

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