Automatic Segmentation of Retinal Layer in OCT Images With Choroidal Neovascularization | IEEE Journals & Magazine | IEEE Xplore

Automatic Segmentation of Retinal Layer in OCT Images With Choroidal Neovascularization


Abstract:

Age-related macular degeneration is one of the main causes of blindness. However, the internal structures of retinas are complex and difficult to be recognized due to the...Show More

Abstract:

Age-related macular degeneration is one of the main causes of blindness. However, the internal structures of retinas are complex and difficult to be recognized due to the occurrence of neovascularization. Traditional surface detection methods may fail in the layer segmentation. In this paper, a supervised method is reported for simultaneously segmenting layers and neovascularization. Three spatial features, seven gray-level-based features, and 14 layer-like features are extracted for the neural network classifier. The coarse surfaces of different optical coherence tomography (OCT) images can thus be found. To describe and enhance retinal layers with different thicknesses and abnormalities, multi-scale bright and dark layer detection filters are introduced. A constrained graph search algorithm is also proposed to accurately detect retinal surfaces. The weights of nodes in the graph are computed based on these layer-like responses. The proposed method was evaluated on 42 spectral-domain OCT images with age-related macular degeneration. The experimental results show that the proposed method outperforms state-of-the-art methods.
Published in: IEEE Transactions on Image Processing ( Volume: 27, Issue: 12, December 2018)
Page(s): 5880 - 5891
Date of Publication: 26 July 2018

ISSN Information:

PubMed ID: 30059302

Funding Agency:


References

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