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Br J Ophthalmol 90:186-190 doi:10.1136/bjo.2004.059824
  • Clinical science
    • Extended reports

A new quality assessment parameter for optical coherence tomography

  1. J S Schuman1,2
  1. 1UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
  2. 2Tufts-New England Medical Center, Tufts University School of Medicine, New England Eye Center, Boston, MA, USA
  3. 3Department of Electrical Engineering and Computer Science and Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
  1. Correspondence to: Joel S Schuman MD, UPMC Eye Center, Department of Ophthalmology, University of Pittsburgh School of Medicine, 203 Lothrop Street, Eye and Ear Institute Suite 816, Pittsburgh, PA 15213, USA; schumanjs{at}upmc.edu
  • Accepted 2 October 2005

Abstract

Aim: To create a new, automated method of evaluating the quality of optical coherence tomography (OCT) images and to compare its image quality discriminating ability with the quality assessment parameters signal to noise ratio (SNR) and signal strength (SS).

Methods: A new OCT image quality assessment parameter, quality index (QI), was created. OCT images (linear macular scan, peripapillary circular scan, and optic nerve head scan) were analysed using the latest StratusOCT system. SNR and SS were collected for each image. QI was calculated based on image histogram information using a software program of our own design. To evaluate the performance of these parameters, the results were compared with subjective three level grading (excellent, acceptable, and poor) performed by three OCT experts.

Results: 63 images of 21 subjects (seven each for normal, early/moderate, and advanced glaucoma) were enrolled in this study. Subjects were selected in a consecutive and retrospective fashion from our OCT imaging database. There were significant differences in SNR, SS, and QI between excellent and poor images (p = 0.04, p = 0.002, and p<0.001, respectively, Wilcoxon test) and between acceptable and poor images (p = 0.02, p<0.001, and p<0.001, respectively). Only QI showed significant difference between excellent and acceptable images (p = 0.001). Areas under the receiver operating characteristics (ROC) curve for discrimination of poor from excellent/acceptable images were 0.68 (SNR), 0.89 (IQP), and 0.99 (QI).

Conclusion: A quality index such as QI may permit automated objective and quantitative assessment of OCT image quality that performs similarly to an expert human observer.