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Automatic Optic Disc and Macula Extraction in Retinal Images Using Curvelet Transform-based Enhancement and Multi-Directional Functors
Retina images have different and vast applications in security systems and medicine such as human identification and eye disease diagnosis. In all of these applications, the need for an automatic system which can recognize and locate eye different parts (such as optical disc (OD) and Macula) as features in the retinal images is quite vital. In this paper, a novel automatic OD and macula extraction algorithm based on Curvelet transform as a multi-scale transforms and multi-directional functor is proposed. In this algorithm, using the introduced multi-directional functor, a brightness structure for the OD and macula extraction is created. In this brightness structure, special brightness patterns for the OD and macula are formed using applying the introduced functor in multiple directions on each pixel of the retinal image. The low contrast retinal images demand for a pre-processing step which the eye components can be distinguished easily from the image background. For this purpose, an effective pre-processing step based on Curvelet transform and a nonlinear adaptive function is utilized. The proposed algorithm is evaluated on available images of the STARE database. The experimental results indicate that the proposed algorithm obtains accuracy rate of 97.5% and 98.7% for the OD and macula extractions on STARE database, respectively.
Optical disc and macula extraction, Curvelet transform, intensity equalizer function, multi-directional functor, retinal image.
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