Neovascularization (NV) is a characteristic of the onset of sight-threatening stage of DR, called proliferative DR (PDR). Identification of PDR requires modeling of these unregulated ill-formed vessels, and other associated signs of PDR. We present an approach that models the micro-pattern of local variations (using texture based analysis) and quantifies structural changes in vessel patterns in localized patches, to arrive at a score of neovascularity. The distribution of patch-level confidence scores is collated into an image-level decision of presence or absence of PDR. Evaluated on a dataset of 779 images combining public data and clinical data from local hospitals, the patch-level neovascularity prediction has a sensitivity of 92.4% at 92.6% specificity. For image-level PDR identification our method is shown to achieve sensitivity of 83.3% at a high specificity operating point of 96.1% specificity, and specificity of 83% at high sensitivity operating point of 92.2% sensitivity. Our approach could have potential application in DR grading where it can localize NVE regions and identify PDR images for immediate intervention.
Keywords: Classification; Computer-assisted screening; Evaluation; Texture.
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