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Simultaneous Segmentation of Retinal OCT Images Using Level Set

  • Bashir Isa DodoEmail author
  • Yongmin Li
  • Muhammad Isa Dodo
  • Xiaohui Liu
Conference paper
  • 36 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1211)

Abstract

Medical images play a vital role in clinical diagnosis and treatments of various diseases. In the field of ophthalmology, Optical coherence tomography (OCT) has become an integral part of the non-invasive advanced eye examination by providing images of the retina in high resolution. Reliable identification of the retinal layers is necessary for the extraction of clinically useful information used for tracking the progress of medication and diagnosing various ocular diseases because changes to retinal layers highly correlate with the manifestation of eye diseases. Owing to the complexity of retinal structures and the cumbersomeness of manual segmentation, many computer-based methods are proposed to aid in extracting useful layer information. Additionally, image artefacts and inhomogeneity of pathological structures of the retina pose challenges by significantly degrading the performance of these computational methods. To handle some of these challenges, this paper presents a fully automated method for segmenting retinal layers in OCT images using a level set method. The method starts by establishing a specific Region of interest (ROI), which aids in handling over- and under-segmentation of the target layers by allowing only the layer and image features to influence the curve evolution. An appropriate level set initiation is devised by refining the edges from the image gradient. Then the prior understanding of the OCT image is utilised in constraining the evolution process to segment seven layers of the retina simultaneously. Promising experimental results have been achieved on 225 OCT images, which show the method converges close to the actual layer boundaries compared to the ground truth images.

Keywords

Image segmentation Level set Evolution constrained optimisation Optical Coherence Tomography Medical image analysis 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Bashir Isa Dodo
    • 1
    Email author
  • Yongmin Li
    • 1
  • Muhammad Isa Dodo
    • 2
  • Xiaohui Liu
    • 1
  1. 1.Brunel University LondonLondonUK
  2. 2.Katsina State Institute of Technology and ManagementKatsinaNigeria

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