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    Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection

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    Author
    Lemaitre, G; Rastgoo, M; Massich, J; Cheung, CY; Wong, TY; Lamoureux, E; Milea, D; Meriaudeau, F; Sidibe, D
    Date
    2016-01-01
    Source Title
    Journal of Ophthalmology
    Publisher
    HINDAWI LTD
    University of Melbourne Author/s
    Lamoureux, Ecosse; Wong, Tien
    Affiliation
    Ophthalmology (Eye & Ear Hospital)
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Lemaitre, G., Rastgoo, M., Massich, J., Cheung, C. Y., Wong, T. Y., Lamoureux, E., Milea, D., Meriaudeau, F. & Sidibe, D. (2016). Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection. JOURNAL OF OPHTHALMOLOGY, 2016, https://doi.org/10.1155/2016/3298606.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/260325
    DOI
    10.1155/2016/3298606
    Abstract
    This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with DME versus normal subjects. Optical Coherence Tomography (OCT) has been a valuable diagnostic tool for DME, which is among the most common causes of irreversible vision loss in individuals with diabetes. Here, a classification framework with five distinctive steps is proposed and we present an extensive study of each step. Our method considers combination of various preprocessing steps in conjunction with Local Binary Patterns (LBP) features and different mapping strategies. Using linear and nonlinear classifiers, we tested the developed framework on a balanced cohort of 32 patients. Experimental results show that the proposed method outperforms the previous studies by achieving a Sensitivity (SE) and a Specificity (SP) of 81.2% and 93.7%, respectively. Our study concludes that the 3D features and high-level representation of 2D features using patches achieve the best results. However, the effects of preprocessing are inconsistent with different classifiers and feature configurations.

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