Intra-retinal Layer Segmentation in Optical Coherence Tomography Using an Active Contour Approach

  • Azadeh Yazdanpanah
  • Ghassan Hamarneh
  • Benjamin Smith
  • Marinko Sarunic
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)

Abstract

Optical coherence tomography (OCT) is a non-invasive, depth resolved imaging modality that has become a prominent ophthalmic diagnostic technique. We present an automatic segmentation algorithm to detect intra-retinal layers in OCT images acquired from rodent models of retinal degeneration. We adapt Chan–Vese’s energy-minimizing active contours without edges for OCT images, which suffer from low contrast and are highly corrupted by noise. We adopt a multi-phase framework with a circular shape prior in order to model the boundaries of retinal layers and estimate the shape parameters using least squares. We use a contextual scheme to balance the weight of different terms in the energy functional. The results from various synthetic experiments and segmentation results on 20 OCT images from four rats are presented, demonstrating the strength of our method to detect the desired retinal layers with sufficient accuracy and average Dice similarity coefficient of 0.85, specifically 0.94 for the the ganglion cell layer, which is the relevant layer for glaucoma diagnosis.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Azadeh Yazdanpanah
    • 1
  • Ghassan Hamarneh
    • 2
  • Benjamin Smith
    • 2
  • Marinko Sarunic
    • 1
  1. 1.School of Engineering Science 
  2. 2.Medical Image Analysis Lab, School of Computing ScienceSimon Fraser UniversityCanada

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