Automatic Feature Extraction Using Replica Based Approach in Digital Fundus Images

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In diabetic retinopathy, exudates play a major part which causes blindness to the diabetic patients. Diabetic patients lost its vision if the exudates will extend to the macular area of the retina. Spreading of this disease on the retina is prevented by automated early detection of presence of exudates. So, it is necessary to prevent the exudates which act as a major challenge in diagnostic task. The presence of exudates is identified based on the variation in grey color presents in retina. The recognition of the optic disc is necessary in the exudates detection procedure while both are related in terms of color, contrast, etc. A various techniques has studied before like morphological approach, region growing approach and so on for the detection of automatic diabetic retinopathy. But the automatic retinal detection leads to a greater chance of loss of sight if the detection is not done properly. So, to enhance the automatic feature extraction in digital fundus images, in this work, we are going to implement replica based approach. Here Optic disk is restricted by the Localized Principal Component Analysis (LPCA) and its shape is detected by a Customized Dynamic Shape Model (CDSM). The extraction of exudates fluid is done by mutual region growing and edge recognition methods. A digital fundus image coordinate system is built to present an enhanced depiction of the features.  The success of the LPCA and CDSM algorithms can be attributed to the utilization of the Replica-Based methods. Performance of the replica based approach is measured in terms of optic disk boundary detection, sensitivity and specificity.
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Fundus Images; Exudates Detection; Automatic Feature Extraction; Optic Disk; Localized Principal Component Analysis; Customized Dynamic Shape Model; Mutual Region Growing; Edge Detection

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