Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid with local binary patterns.

Yu Ying Liu, Mei Chen, Hiroshi Ishikawa, Gadi Wollstein, Joel S. Schuman, James M. Rehg

    Research output: Contribution to journalArticle


    We address a novel problem domain in the analysis of optical coherence tomography (OCT) images: the diagnosis of multiple macular pathologies in retinal OCT images. The goal is to identify the presence of normal macula and each of three types of macular pathologies, namely, macular hole, macular edema, and age-related macular degeneration, in the OCT slice centered at the fovea. We use a machine learning approach based on global image descriptors formed from a multi-scale spatial pyramid. Our local descriptors are dimension-reduced Local Binary Pattern histograms, which are capable of encoding texture information from OCT images of the retina. Our representation operates at multiple spatial scales and granularities, leading to robust performance. We use 2-class Support Vector Machine classifiers to identify the presence of normal macula and each of the three pathologies. We conducted extensive experiments on a large dataset consisting of 326 OCT scans from 136 patients. The results show that the proposed method is very effective.

    Original languageEnglish (US)
    Pages (from-to)1-9
    Number of pages9
    JournalMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
    Issue numberPt 1
    StatePublished - Nov 18 2010


    ASJC Scopus subject areas

    • Medicine(all)

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