Abstract
Optical Coherence Tomography (OCT) is a well-established medical imaging technique that allows the analysis of the eye fundus characteristics in real time. These images enable the experts to make a clinical evaluation of the retinal vasculature, whose morphology provides relevant information for diseases like diabetes, hypertension or arteriosclerosis. In this paper, we present a novel proposal for the automatic vasculature identification in retinal OCT images. To achieve this, we analyse the intensity profiles between representative retinal layers, previously segmented. Then, two statistical models are generated using representative samples of vessel and non-vessel profiles. The analysis of both statistical models let us optimize the discrimination of both cathegories that is used, finally, to identify the vessel locations. The proposed method was adjusted and validated using 256 OCT images, including 1274 vascular structures that were labelled by an expert clinician. Satisfactory results were provided as a precision of 94.55% and a recall of 90.25% were obtained, respectively. The method facilitates the doctors’ work allowing better analysis and treatments of vascular diseases.
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