Automated stromal nerve rejection in corneal confocal images in vivo

Eric N. Brown, Jon J. Camp, Sanjay V. Patel, Jay W. McLaren, William M. Bourne, Richard A. Robb

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


With the advent of corneal confocal microscopy, investigators can determine keratocyte density in the corneal stroma in vivo. We and others have written automated algorithm to measure keratocyte density from human corneal confocal images. Such algorithms are only accurate if they exclude images of stromal nerve bundles (elongated objects) that would otherwise be counted as keratocytes. In this study we devised an algorithm to identify stromal nerve bundles and exclude them from measurements of keratocyte density. Nerve bundles were detected based on their size and aspect ratio, and were then subtracted from images by using a combination of morphology operations and direction calculations. The validity of nerve removal on measurements of keratocyte density was assessed. Keratocyte density was measured from confocal images of three normal human corneas in vivo by using our algorithm with nerve removal. After the same eyes underwent enucleation, density was measured manually from histologic sections. Keratocyte density was also measured from confocal images of 57 normal corneas in vivo (57 subjects) with and without nerve removal. In the three enucleated eyes, there was no significant difference between keratocyte density measured by automated counting with nerve removal and by histologic methods (P = 0.75). However, in the 57 normal corneas, use of the nerve-removal algorithm reduced estimates of density by 57.0±164.6 cells/mm3 (mean±SD, p<0.038) in the anterior two-thirds of the stroma.

Original languageEnglish (US)
Pages (from-to)I/-
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Jan 1 2000
EventMedical Imaging 2000: Image Processing - San Diego, CA, USA
Duration: Feb 14 2000Feb 17 2000

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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