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Automatic Segmentation of Retinal Vasculature Detection of Diabetic Retinopathy for Early using SVM
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 111 - Number 2 |
| Year of Publication: 2015 |
| Authors: Sayyada Sara Banu, Mohammed Waseem Ashfaque, Perumal Uma |
10.5120/19510-1127
|
Sayyada Sara Banu, Mohammed Waseem Ashfaque, Perumal Uma . Automatic Segmentation of Retinal Vasculature Detection of Diabetic Retinopathy for Early using SVM. International Journal of Computer Applications. 111, 2 ( February 2015), 24-28. DOI=10.5120/19510-1127
Abstract
Detection of Micro aneurysm at an early stage is the first step in preventing Diabetic Retinopathy, Diabetic retinopathy (DR) is the most common cause of blindness. The visual impairment can be avoided by detecting DR. Segmentation of retinal structures help in the diagnosis of DR. In this work, anatomical structures such as blood vessels, exudates and micro aneurysms in retinal images are segmented and the images are classified as normal or DR images by extracting features from these structures and the Gray Level Co-occurrence Matrix (GLCM). These extracted of candidates is the problem domain for the Support Vector Machine classifier. The Support Vector Machine classifier classifies the images to correctly determine the findings of candidate extraction to be microaneursym or not. The simulations of the algorithms are done and the results are shown. The classifier used is Support Vector Machine (SVM) which gives an average accuracy of 96%.
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