Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding

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

    Research output: Contribution to journalArticlepeer-review

    Abstract

    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 edema, macular hole, 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 features are dimension-reduced local binary pattern histograms, which are capable of encoding texture and shape information in retinal OCT images and their edge maps, respectively. 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. To further discriminate sub-types within a pathology, we also build a classifier to differentiate full-thickness holes from pseudo-holes within the macular hole category. We conduct extensive experiments on a large dataset of 326 OCT scans from 136 subjects. The results show that the proposed method is very effective (all AUC. >. 0.93).

    Original languageEnglish (US)
    Pages (from-to)748-759
    Number of pages12
    JournalMedical Image Analysis
    Volume15
    Issue number5
    DOIs
    StatePublished - Oct 2011

    Keywords

    • Computer-aided diagnosis (CAD)
    • Local binary patterns (LBP)
    • Macular pathology
    • Multi-scale spatial pyramid (MSSP)
    • Optical coherence tomography (OCT)

    ASJC Scopus subject areas

    • Radiological and Ultrasound Technology
    • Radiology Nuclear Medicine and imaging
    • Computer Vision and Pattern Recognition
    • Health Informatics
    • Computer Graphics and Computer-Aided Design

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