A novel automatic segmentation of healthy and diseased retinal layers from OCT scans

A. El Tanboly, M. Ismail, A. Switala, M. Mahmoud, A. Soliman, T. Neyer, A. Palacio, A. Hadayer, M. El-Azab, S. Schaal, A. El-Baz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

This paper introduces a novel framework for segmenting retinal layers from optical coherence tomography (OCT) images. In order to account for the noise and inhomogeneity of OCT scans, especially for diseased ones, the proposed framework is based on unique joint model that combines shape, intensity, and spatial information, and is able to segment 12 distinct retinal layers. First, the shape prior is built using a subset of co-aligned training OCT images. The alignment process is initialized using an innovative method that employs multi-resolution edge tracking which defines control points on the tracked retinal boundaries. The shape model is then adapted during the segmentation process using visual appearance characteristics that are described using pixel-wise image intensities and their spatial interaction features. In order to more accurately model the empirical grey level distribution of OCT images, a linear combination of discrete Gaussians (LCDG) is used that has positive and negative components. Also, in order to accurately account for noise, the model is integrated with a second-order Markov Gibbs random field (MGRF) spatial interaction model. The proposed approach was tested on 200 normal and diseased OCT scans (e.g. Age macular degeneration (AMD), diabetic retinopathy), having their ground truth delineated by retina specialists, then measured using the Dice similarity coefficient (DSC), agreement coefficient (AC1), and average deviation (AD) metrics. The accuracy achieved by the segmentation approach clearly demonstrates the promise it holds for robust segmentation of retinal layers which would aid in the early diagnosis of different retinal abnormalities.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages116-120
Number of pages5
ISBN (Electronic)9781467399616
DOIs
StatePublished - Aug 3 2016
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: Sep 25 2016Sep 28 2016

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2016-August
ISSN (Print)1522-4880

Other

Other23rd IEEE International Conference on Image Processing, ICIP 2016
Country/TerritoryUnited States
CityPhoenix
Period9/25/169/28/16

Keywords

  • LCDG
  • MGRF
  • OCT
  • Reflectivity
  • Shape prior

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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