Statistics of optical coherence tomography data from human retina

Norberto Mauricio Grzywacz, Joaquín De Juan, Claudia Ferrone, Daniela Giannini, David Huang, Giorgio Koch, Vincenzo Russo, Ou Tan, Carlo Bruni

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


Optical coherence tomography (OCT) has recently become one of the primary methods for noninvasive probing of the human retina. The pseudoimage formed by OCT (the so-called B-scan) varies probabilistically across pixels due to complexities in the measurement technique. Hence, sensitive automatic procedures of diagnosis using OCT may exploit statistical analysis of the spatial distribution of reflectance. In this paper, we perform a statistical study of retinal OCT data. We find that the stretched exponential probability density function can model well the distribution of intensities in OCT pseudoimages. Moreover, we show a small, but significant correlation between neighbor pixels when measuring OCT intensities with pixels of about 5 μm. We then develop a simple joint probability model for the OCT data consistent with known retinal features. This model fits well the stretched exponential distribution of intensities and their spatial correlation. In normal retinas, fit parameters of this model are relatively constant along retinal layers, but varies across layers. However, in retinas with diabetic retinopathy, large spikes of parameter modulation interrupt the constancy within layers, exactly where pathologies are visible. We argue that these results give hope for improvement in statistical pathology-detection methods even when the disease is in its early stages.

Original languageEnglish (US)
Article number5432977
Pages (from-to)1224-1237
Number of pages14
JournalIEEE Transactions on Medical Imaging
Issue number6
StatePublished - Jun 1 2010
Externally publishedYes


  • Diabetic retinopathy
  • Maximum likelihood detection
  • Optical coherence tomography
  • Stretched exponential distribution
  • Visual system

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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